ICINCO 2023 Abstracts

Area 1 - Industrial Informatics

Full Papers

Paper Nr:	60
Title:	Evaluation of Controllability of Interaction Between Pedestrian and Autonomous Mobile Robot in Shared Mobility Space
Authors:	Kentaro Sugiura, Mizuho Aoki, Kazuhide Kuroda, Hiroyuki Okuda and Tatsuya Suzuki
Abstract:	Recently, a growing number of autonomous mobile robots (AMR) coexisting with humans are being introduced in many types of AMR-human shared space. Such AMR often needs to be navigated in narrow spaces while smoothly interacting with pedestrians. In such a situation, AMRs are highly recommended to estimate the pedestrian’s intentions and take appropriate action from the viewpoint of social acceptance. First, this paper presents new modeling and understanding of pedestrian behavior, particularly focusing on decision-making when they face an AMR at a close distance. Real-world experiments were conducted using a remote switch to directly record their decisions, and a mathematical decision model is made by using a logistic regression model. In the interaction between AMR and pedestrians, the AMR is expected to ‘implicitly control’ the interacting pedestrian by changing its own action. From this perspective, the influence of the AMR motion on the pedestrian’s decision is formally defined and calculated by using the controllability Gramian of the augmented AMR-pedestrian system model. A deep understanding of the influence of AMR action on pedestrian behavior will be beneficial to develop control policies for smooth AMR-pedestrian interactions.
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Paper Nr:	112
Title:	Network Analysis of the Egyptian Reddit Community
Authors:	Samy Shaawat, Adham Hammad, Karim Farhat, Mina Thabet and Walid Gomaa
Abstract:	This paper presents a network analysis of the Reddit community focused on Egypt. We collected and constructed a comprehensive dataset consisting of 23,185 users and 105 Egyptian subreddits. Through network analysis criteria such as degree analysis, degree distribution analysis, and clustering coefficient analysis, we explored the structural properties, connectivity patterns, and local clustering within the Egyptian Reddit network. The findings provide insights into the community dynamics, influential users, and information flow within the network. Our study contributes to a better understanding of online communities in the context of Egypt and sheds light on the relationships and interactions within the Egyptian Reddit community. By leveraging network analysis techniques, we uncover the importance of individual nodes, the distribution of node degrees, and the formation of tightly knit groups.This study contributes significantly to the understanding of online communities specific to Egypt, shedding light on relationships and interactions within the Egyptian Reddit community.
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Short Papers

Paper Nr:	16
Title:	Schedulling Production Based on an Optimized Production Sequencer and Manufacturing Maps
Authors:	Nuria Rosillo Guerrero, Nicolás Montés Sánchez, Antonio Falcó Montesinos, Eduardo Garcia Magraner and Judith Vizcaino Hilario
Abstract:	In this article, we present an innovative application of manufacturing maps, specifically combining Petri Nets and Miniterms. Our proposed algorithm enables the determination of an optimal manufacturing sequence based on real-time information from the manufacturing line. The primary objective of this algorithm is to minimize the disparity in cycle times between different models, aiming to minimize the duration of workstations being stopped or blocked. This optimization leads to a reduction in total production time, accompanied by various benefits such as energy savings and increased production. To validate our approach, we implemented the algorithm using manufacturing maps and applied it to the 8XY line—a multimodel welding line located at the Ford factory in Almussafes, Valencia. We conducted simulations using actual production data from the Ford factory, considering three different types of order: random, optimal, and unfavorable. The goal was to compare the production time for each sequence. The results obtained from the simulations demonstrated a significant time improvement when employing the optimal sequence, as outlined in the article. A comprehensive analysis of the three sequences studied is provided. As a future direction of this research, we intend to explore additional applications that can leverage manufacturing maps for production line optimization. For instance, we plan to investigate the determination of optimal sequences for anomalies, where improvements in the line to reduce cycle time could yield greater profitability. Moreover, we aim to explore how production lines can be dynamically rebalanced in real-time to achieve energy savings and other advantages. These potential extensions highlight the versatility and practical implications of manufacturing maps in enhancing production line efficiency.
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Paper Nr:	42
Title:	Hybrid LSTM-Fuzzy System to Model a Sulfur Recovery Unit
Authors:	Jorge S. S. Júnior, Jérôme Mendes, Francisco Souza and Cristiano Premebida
Abstract:	Dealing with the dynamics of an industrial process using machine learning techniques has been a paradigm throughout decades of technological advancement. Motivated by addressing this problem, the present work proposes the hybridization of a neo-fuzzy neuron system (NFN) with a long short-term memory network (LSTM), the NFN-LSTM model. The fuzzy part guarantees interpretability through linguistic terms associated with membership functions that allow an effective mapping of the input variables in its universe of discourse with respect to the output. On the other hand, the LSTM part explores high-level representations useful for sequential data in dynamic processes. In this work, a sulfur recovery unit is used as a case study, whose dynamics are mainly associated with peak values in the estimation of residual hydrogen sulfide. The proposed NFN-LSTM model is compared with state-of-the-art methods, such as standalone LSTM, GAM-ZOTS (generalized additive models using zero-order Takagi-Sugeno fuzzy system), iMU-ZOTS (extension of GAM-ZOTS), ALMMo-1 (autonomous learning of a multimodel system from streaming data), iNoMO-TS (iterative learning of multivariate fuzzy models using novelty detection), and SVR (support vector regression). Analyzing the results, the proposed model performed similarly to standalone LSTM, and both outperformed the other methods. Finally, NFN-LSTM manages to balance interpretability and accuracy.
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Paper Nr:	104
Title:	Application of a Simulation Platform for the Study and Experimental Comparison of PEM Electrolyzer Models
Authors:	Antonio J. Calderón, Francisco J. Folgado, David Calderón and Isaías González
Abstract:	In the last decades, hydrogen has been a trend in the energy sector as it has been employed as an energy carrier in applications based on Renewable Energy Sources (RES). In this context, RES-based smart grids and microgrids use devices called electrolyzers to generate hydrogen. The implementation of this device in a real installation faces difficulties due to its complex operation and the diversity of variables involved. Therefore, a prior study is essential to understand the behavior of these devices and to achieve correct implementation and management. This paper describes the application of a simulation platform for the study of Proton Exchange Membrane Electrolyzers (PEMEL), as well as the comparison of the data obtained through simulation and those reported from an experimental PEMEL operating within a RES-powered smart microgrid hybridized with green hydrogen. The principle of operation of the simulation platform is presented together with the models selected for this research. The experimental PEMEL is framed in the operation of the smart microgrid, where its automation equipment and the interaction between them are described. Furthermore, the process followed to obtain the simulated and experimental data is detailed. Finally, a case study is reported where simulated and experimental results are compared.
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Paper Nr:	134
Title:	Analysis of Powder Behavior Inside the Mortar During Tableting Process
Authors:	Yosuke Tachikawa, Tetsu Kamiya and Takanori Yamazaki
Abstract:	Tableting machines are used to make tablets from food, pharmaceutical, and other powders. It is well known that the quality of tablets formed by tableting machines varies greatly depending on the compression conditions, such as compression speed and compression force. Therefore, it is important to clarify the behavior of powder inside the mortar during the compaction process. In this present research, we designed and manufactured a thin-walled cylindrical mortar. A special strain gage was attached to the mortar to measure the force acting on the mortar wall during tableting. Based on these results, a discrete element method (DEM) simulation is performed, we compare and discuss the behavior of powder inside the mortar during the tableting process.
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Paper Nr:	138
Title:	Relationship Between Tableting Motion and Tablet Hardness in Compression Molding
Authors:	Shinji Kobayashi, Takahiro Sato and Takanori Yamazaki
Abstract:	A tableting machine is used to form powders into tablets. It is well known that the quality of tablets formed by tableting machines varies greatly depending on the compression conditions, such as compression velocity and compressive force. It is of industrial importance to clarify how compression conditions affect the properties of the formed tablets. In this research, we manufactured a tableting machine with an upper and lower pestle that can be arbitrarily operated, and the aim of this research is to clarify the relationship between the tableting conditions and the property of the formed tablets. In this experiment, we change the driving pestle as the tableting condition, measure the formed tablet hardness, and discuss the relationship between them.
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Paper Nr:	146
Title:	Cycle Life Prediction of Lithium-Ion Batteries Using Deep Learning
Authors:	Yu Fujitaki and Hiroyuki Kobayashi
Abstract:	To improve the accuracy of lithium-ion battery life prediction, we decided to train multiple LSTMs separately, as each battery may have its own unique characteristics. When verifying the results, we found similarities between the verification and training batteries and used LSTMs to predict the verification battery, but we show that the results were not successful.
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Paper Nr:	160
Title:	Design of Acceleration Command for Feed Drive System in Corner Motion
Authors:	Yuki Nomura, Kazuma Tanaka and Takanori Yamazaki
Abstract:	CNC (Computer Numerical Control) machine tools are required to have high accuracy and production efficiency. CNC machine tools generally generate trajectories such as position and speed within the NC system for commands (usually G code), and then drive each axis. However, in actual contouring motion, the machine often does not move perfectly as commanded, due to tracking errors such as response delays in the control system. NC device manufacturers seem to apply deceleration process to reduce these errors, but their methods have not been disclosed. In this research, we focused on contouring motion with steep acceleration/deceleration, discussed the contouring accuracy when driving the feed drive mechanism with the acceleration/deceleration command generated by the motion controller and our proposed method. Typical NC control controller for machine tools generate trapezoidal or S shaped acceleration/deceleration commands. We propose a command design method based on the Preshaping method which is also known as a vibration suppression method and report the contouring accuracy when applying this method.
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Paper Nr:	167
Title:	Study on Cost Estimation of the External Fleet Full Truckload Contracts
Authors:	Jan Kaniuka, Jakub Ostrysz, Maciej Groszyk, Krzysztof Bieniek, Szymon Cyperski and Paweł D. Domański
Abstract:	Goods shipping supports the operation and the development of the global economy. As there are thousands of logistics companies, there exists a big need for solutions for their daily operation. The shipment can be carried out in many ways. This work focuses on the road transportation in form of the Full Truck Load (FTL). Once the service is supported by the third party, there is a need to have a tool that compares various offers and allows to estimate the cost. Generally, FTLs are used in the long range routes and the estimation of such contracts can be handled in many ways starting from the simple calculators up to data based machine learning solutions. Nonetheless, the need for the cost estimation appears for the short routes, which often support long range ones. Their pricing rules differs from the long range ones and the required approaches should differ as well. This work presents the wide comparison of 35 regression and machine learning approaches applied to the task. The assessment is performed using real contract data of several companies operating in Europe.
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Area 2 - Intelligent Control Systems and Optimization

Full Papers

Paper Nr:	13
Title:	A Dynamic Computational Model of Head Sway Responses in Human Upright Stance Postural Control During Support Surface Tilt
Authors:	Vittorio Lippi, Christoph Maurer and Stefan Kammermeier
Abstract:	Human and humanoid posture control models usually rely on single or multiple degrees of freedom inverted pendulum representation of upright stance associated with a feedback controller. In models typically focused on the action between ankles, hips, and knees, the control of head position is often neglected, and the head is considered one with the upper body. However, two of the three main contributors to the human motion sensorium reside in the head: the vestibular and the visual system. As the third contributor, the proprioceptive system is distributed throughout the body. In human neurodegenerative brain diseases of motor control, like Progressive Supranuclear Palsy PSP and Idiopathic Parkinson’s Disease IPD, clinical studies have demonstrated the importance of head motion deficits. is work sp ecifically addresses the control of the head during a perturbed upright stance. A control model for the neck is proposed following the hypothesis of a modular posture control from previous studies. Data from human experiments are used to fit the model and retrieve sets of parameters representative of the behavior obtained in different conditions. e result of the analysis is twofold: validate the model and its underlying hypothesis and provide a system to assess the differences in posture control that can be used to identify the differences between healthy subjects and patients with different conditions. Implications for clinical pathology and application in humanoid and assistive robotics are discussed.
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Paper Nr:	65
Title:	Locally Convex Neural Lyapunov Functions and Region of Attraction Maximization for Stability of Nonlinear Systems
Authors:	Lucas Hugo, Philippe Feyel and David Saussié
Abstract:	The Lyapunov principle involves to find a positive Lyapunov function with a local minimum at the equilibrium point, whose time derivative is negative with a local maximum at that point. As a validation, it is usual to check the sign of the Hessian eigenvalues which can be complex: it requires to know a formal expression of the system dynamics, and especially a differentiable one. In order to circumvent this, we propose in this paper a scheme allowing to validate these functions without computing the Hessian. Two methods are proposed to force the convexity of the function near the equilibrium; one uses a neural single network to model the Lyapunov function, the other uses an additional one to approximate its time derivative. The training process is designed to maximize the region of attraction of the locally convex neural Lyapunov function trained. The use of examples allows us to validate the efficiency of this approach, by comparing it with the Hessian-based approach.
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Paper Nr:	67
Title:	Nonlinear Model Predictive Control for Uranium Extraction-Scrubbing Operation in Spent Nuclear Fuel Treatment Process
Authors:	Duc-Tri Vo, Ionela Prodan, Laurent Lefèvre, Vincent Vanel, Sylvain Costenoble and Binh Dinh
Abstract:	This paper addresses the particularities of the uranium extraction-scrubbing operation in a spent nuclear fuel treatment process (PUREX-Plutonium Uranium Refining by Extraction) through the use of set-point tracking MPC (Model Predictive Control). The presented controller uses the feed solution flow rate as the manipulated variable to control the saturation of the solvent at the extraction step. In addition, it guarantees not to loose uranium in the raffinates, and ensures equipment limitations during operation time. Simulation results show that the tracking NMPC effectively ensures accurate set point tracking and constraints guarantee. As a result, the system can be driven to its optimal working condition, avoid and recover from constraint violations. The control performance was compared with PID and openloop controllers.
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Paper Nr:	91
Title:	Positively Invariant Sets for ODEs and Numerical Integration
Authors:	Peter Giesl, Sigurdur Hafstein and Iman Mehrabinezhad
Abstract:	We show that for an ordinary differential equation (ODE) with an exponentially stable equilibrium and any compact subset of its basin of attraction, we can find a larger compact set that is positively invariant for both the dynamics of the system and a numerical method to approximate its solution trajectories. We establish this for both one-step numerical integrators and multi-step integrators using sufficiently small time-steps. Further, we show how to localize such sets using continuously differentiable Lyapunov-like functions and numerically computed continuous, piecewise affine (CPA) Lyapunov-like functions.
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Paper Nr:	92
Title:	Maritime Dynamic Resource Allocation and Risk Minimization Using Visual Analytics and Elitist Multi-Objective Optimization
Authors:	Mayamin H. Raha, Md. A. Sayed, Monica Nicolescu, Mircea Nicolescu and Sushil Louis
Abstract:	Enhancing the safety of protected regions around Navy vessels is one of the most challenging research topics in maritime domains. Robust tactical resource allocation depends on understanding of how the placement, configurations, orientations of multiple assets affect both the area and intensity of coverage around the ships. Towards this end, we built a unique resource allocation problem where we apply a randomized genetic algorithm for searching through a space of 2144 possible parameters representing area coverage, orientation of 6 tactical assets. Our elitist genetic algorithm yielded a maximum fitness value of 90%, 98%, 100% within 50, 150 and 300 generations respectively. Moreover, we put forward a distinctive constrained dynamic resource allocation problem specific to USS Arleigh Burke Destroyer model (DDG-51), where the assets are defenses and coastal guards having binoculars. To solve this, we have used a cross-generational elitist selection based evolutionary algorithm (EA) where our objective is to maximize area of coverage and minimize risk simultaneously. It is a non-deterministic polynomial-time hard (NP-Hard) problem which required searching through a space of 2 48 parameters and resulted in a fitness value of 98% within 35 generations. Furthermore, we present two novel visualization techniques addressing both types of resource allocations.
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Paper Nr:	97
Title:	Stereo Video Camera Calibration in the Wild
Authors:	Arhum Sultana and Michael Jenkin
Abstract:	Although a number of robust stereo camera calibration algorithms exist in the literature, a common assumption of these algorithms is a representative set of calibration images containing a planar calibration target of known geometry. For stereo-video applications, it is a common practice to obtain a large number of stereo image pairs for the stereo calibration process. How should an optimal set of stereo-video calibration images be chosen when controlled camera positioning is difficult or impossible? Here we demonstrate how a greedy RANdom SAmple Consensus (RanSaC)-based approach can be used to choose the appropriate calibration image set for improved stereo camera calibration. This paper describes the performance of a greedy, RanSaC approach which is compared against a random frames selection approach. Performance is measured through mean calibration reprojection error. Evaluation on real world stereo video calibration data-sets collected in the underwater environment illustrates the effectiveness of the proposed approach.
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Paper Nr:	107
Title:	Enhanced Optimal Beacon Placement for Indoor Positioning: A Set Variable Based Constraint Programming Approach
Authors:	Sven Löffler, Ilja Becker, Carlo B. ückert and Petra Hofstedt
Abstract:	Indoor localization is of increasing importance in various environments, including hospitals, retirement homes, and emergency situations. To achieve efficient and accurate positioning of mobile individuals indoors, the optimized distribution of sensors is crucial. The task of manually placing beacons (sensors) for indoor positioning in a building can be challenging and time-consuming. Several researchers have tackled this issue using different algorithms and considering various use cases. In our previous work (Löffler et al., 2022) at the ACS/IEEE International Conference on Computer Systems and Applications (AICCSA 2022), we introduced a novel approach that leverages constraint programming with exclusively Boolean variables to efficiently place Bluetooth Low Energy (BLE) beacons in indoor scenarios. We evaluated the quality of our results by comparing them against manually optimized beacon placement and assessing their performance in a real-world building. This paper extends the findings of (L öffler et al., 2022) by introducing a new constraint-based approach that incorporates only set variables and Boolean variables, a more elaborate and balanced evaluation, i.e. on further buildings, and certain refinements of our overall method.
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Paper Nr:	109
Title:	Neural-Network for Position Estimation of a Cable-Suspended Payload Using Inertial Quadrotor Sensing
Authors:	Julien Mellet, Jonathan Cacace, Fabio Ruggiero and Vincenzo Lippiello
Abstract:	This paper considers a standard quadrotor drone with a cable-suspended payload and minimal sensor configuration. A neural network estimator is proposed to perform accurate real-time payload position estimation. A novel proprioceptive feedback measurement method is proposed, and a neural network has been trained with domain randomization. The network shows accurate zero-shot estimation, even with excitations never seen by the system before. This preliminary work has been tested in a simulated environment and aims to show that only onboard inertial sensing is enough to achieve the sought task. The presented work may open new applications for drone transportation in real environments subject to several perturbations.
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Paper Nr:	151
Title:	Variable Trust Setting for Safe and Ethical Algorithms for Navigation of Autonomous Vehicles (C-NAV) on a Highway
Authors:	Joshua D’Souza, Jisun Kim and James E. Pickering
Abstract:	This paper presents the use of an ethical model-to-decision approach for promoting safe manoeuvrability of autonomous vehicles (AVs) on highways, when considering scenarios such as exiting a highway via a slip road. In this research, a modelling and simulation approach is undertaken. The modelling involves the use of an adaptive model-predictive control (MPC) algorithm with a dynamic bicycle model. The approach was developed to incorporate a novel continuous evaluation of the distances between AVs (considering virtual boundaries), logical sequences towards achieving safe lane change and slip road exit manoeuvres (driving rules based on deontological ethics), and control logic towards accounting for acceleration, deceleration, and constant velocity. Based on this, a novel continuous risk assessment algorithm has been developed based on the product of collision probability and harm. This has been used to investigate the introduction of a novel trust setting that gives the user ‘control’ of how the AV operates around other AVs. The results presented in the paper highlight the effectiveness of the approach, i.e., the ability to undertake ethical and safe manoeuvres in the event of difficult highway decision scenarios such as slip road exits.
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Paper Nr:	170
Title:	Explainable Machine Learning for Evapotranspiration Prediction
Authors:	Bamory T. Koné, Rima Grati, Bassem Bouaziz and Khouloud Boukadi
Abstract:	The current study aims to develop efficient machine learning models that can accurately predict potential evapotranspiration, an essential parameter in agricultural water management. Knowing this value in advance can facilitate proactive irrigation scheduling. Two models, Long Short-Term Memory and eXtreme Gradient Boosting, are evaluated using performance metrics such as mean squared error, mean average error, and root mean squared error. One of the challenges with these models is their lack of interpretability, as they are often referred to as ”black-boxes.” To address this issu, the study provides global explanations for how the best-performing model learns. Additionally, the study incrementally improves the model’s performance based on the provided explanations. Overall, the study contributes to developing more accurate and interpretable machine learning models for predicting potential evapotranspiration, which can improve agricultural water management practices.
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Short Papers

Paper Nr:	11
Title:	Technological Solution for Crime Prevention in Los Olivos
Authors:	Juan-Pablo Mansilla, Matías Beteta and David Castañeda
Abstract:	This research proposes a technological solution for citizen security and crime prevention based on machine learning in the district of Los Olivos, which alerts if the area in which a citizen is located is unsafe, showing a probability of the level of insecurity in each area, making more visible the areas with the highest level of insecurity; this was achieved using a machine Learning model, with the Naive Bayes algorithm exactly. A sample of 108 users was used for validation, with whom the technological solution was tested using a test scenario. In this sense, a questionnaire was elaborated to evaluate the perception of the users with an acceptance level of 93.5%. On the other hand, when using the Naive Bayes algorithm is ensured to obtain a better “Accuracy” and distribution by category in comparison with the following algorithms: classification forest, carboost classifier and KNN respectively. Therefore, it was with the use of one the Naive Bayes algorithm that the technological solution was carried out. The technological solution proposed is innovative for Peru because it uses machine learning as a technology. In addition, this solution could be replicated in any other district of Metropolitan Lima.
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Paper Nr:	30
Title:	Experimental Investigation and Comparison of Approaches for Correcting Acceleration Phases in Motor Torque Signal of Electromechanical Axes
Authors:	Chris Schöberlein, André Sewohl, Holger Schlegel and Martin Dix
Abstract:	Electromechanical axes are an essential factor for productivity in almost all common production systems. In context of Industry 4.0, using integrated sensors for machine monitoring is gaining importance in recent years. In addition to the well-known condition monitoring of mechanical components, the internal control loop signals are capable to estimate external load forces, e.g. caused by production process. However, this requires the separation of all motor-related signal components from the external loads. The paper contributes to this topic by comparing multiple approaches for detecting acceleration and braking phases during conventional axis movements and examines the subsequent correction of associated components in motor torque signal. All approaches exclusively use signals available in the drive and control system. Extensive experiments on a single-axis rotary test rig show general suitability as well as limitations of the presented methods.
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Paper Nr:	46
Title:	Probabilistic Physics-Augmented Neural Networks for Robust Control Applied to a Slider-Crank System
Authors:	Edward Kikken, Jeroen Willems, Rob Salaets and Erik Hostens
Abstract:	Key industrial trends such as increasing energy and performance requirements as well as mass customization, lead to more complex non-linear machines with many variants. For such systems, variability in dynamics arising from many factors significantly affects performance. To capture this adequately we introduce a probabilistic extension of a Physics-Augmented Neural Network (PANN). We subsequently illustrate the added value of such models in robust optimal control, thereby keeping performance high while guaranteeing to meet the application’s constraints. The approach is validated on a experimental slider-crank mechanism, which is ubiquitous in industrial machines.
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Paper Nr:	48
Title:	Mapping, Localization and Navigation for an Assistive Mobile Robot in a Robot-Inclusive Space
Authors:	Prabhu R. Naraharisetti, Michael A. Saliba and Simon G. Fabri
Abstract:	Over the years, the major advancements in the field of robotics have been enjoyed more by the mainstream population, e.g. in industrial and office settings, than by special groups of people such as the elderly or persons with impairments. Despite the advancement in various technological aspects such as artificial intelligence, robot mechanics, and sensors, domestic service robots are still far away from achieving autonomous functioning. One of the main reasons for this is the complex nature of the environment and the dynamic nature of the people living inside it. In our laboratory, we have started to address this issue with our minimal degrees of freedom MARIS robot, by upgrading it from a teleoperated robot to an autonomous robot that can operate in a robot-inclusive space that is purposely designed to adopt algorithms that are not very computationally intensive, and hardware architecture that is relatively simple. This paper discusses the implementation of suitable SLAM algorithms, to select the best method for mapping and localization of the MARIS robot in this robot-inclusive environment. The emphasis is on the development of low-complexity algorithms that can map the environment with lesser errors. The paper also discusses the 3D mapping, and the ROS based navigation stack implemented on the MARIS robot, using just a LiDAR, a Raspberry Pi processor, and DC motors with encoders as main hardware architecture, so as to keep low costs.
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Paper Nr:	56
Title:	Data Digitalization and Conformity Verification in Oil and Gas Industry Databooks Using Semantic Model Based on Ontology
Authors:	Mario R. Nascimento Marques Junior, Eder Mateus Nunes Gonçalves, Silvia C. Botelho and Emanuel D. Estrada
Abstract:	Databooks are essential for monitoring and validating construction projects in the oil industry, containing crucial information like quality certificates and technical reports. However, manual analysis of these databooks is time-consuming, labor-intensive, and error-prone. This study proposes an intelligent system to streamline databook search and validation, enhancing efficiency and accuracy. Developing a valid conceptual model for databooks and their components presents a significant challenge. To overcome this, we focus on acquiring semantics for databooks and utilizing a semantic model for compliance checks. We introduce an ontology designed specifically for verifying completeness and compliance in Brazilian oil industry documents, encompassing domain knowledge and verification processes. Using the Methontology methodology, we create the ontology and integrate it with an annotation tool to validate its ability to incorporate semantic structures and facilitate compliance verification. Comparative analysis with manual verification by experts shows identical outcomes, confirming the effectiveness of the automated compliance checking process. The ontology-based approach offers advantages such as time savings, enhanced accuracy, and simplified work for specialists. This study contributes to oil industry document analysis by providing a semantic model that streamlines databook verification, with potential applications for compliance verification of complex documents in various domains.
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Paper Nr:	61
Title:	Kinematics Based Joint-Torque Estimation Using Bayesian Particle Filters
Authors:	Roja Zakeri and Praveen Shankar
Abstract:	The aim of this paper is to estimate unknown torque in a 7-DOF industrial robot using Bayesian approach by observing the kinematic quantities. This paper utilizes two PMCMC algorithms (Particle Gibbs and Particle MH algorithms) for estimating unknown parameters of Baxter manipulator including joint torques, measurement and noise errors. The SMC technique has been used to construct the proposal distribution at each time step. The results indicate that for the Baxter manipulator, both PG and PMH algorithms perform well, but PG performs better as the estimated parameters using this technique have less deviation from the true parameters value. And this is due to sampling from parameters conditional distributions.
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Paper Nr:	71
Title:	Contraction Metrics by Numerical Integration and Quadrature: Uniform Error Estimate
Authors:	Peter Giesl, Sigurdur Hafstein and Iman Mehrabinezhad
Abstract:	We show that contraction metrics for continuous time dynamical systems can be computed numerically using numerical integration of certain initial value problems with a subsequent numerical quadrature. Further, we show that for any compact subset of an equilibrium’s basin of attraction and any ε > 0, the parameters for the numerical methods, i.e. the integration interval and the step-size, can be chosen such that the error in the contraction metric is less than ε at any point in the compact subset. These results will be used as a part of a numerical method to rigorously compute contraction metrics.
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Paper Nr:	73
Title:	Dynamic Periodic Event-Triggered Control for Linear Systems Based on Partial State Information
Authors:	Mahmoud Abdelrahim and Dhafer Almakhles
Abstract:	We are interested in the design of satabilizing event-driven controllers for linear time-invariant systems. We assume that the plant state is partially known and the feedback signal is sent to the controller at discrete-time instants via a digital channel and we synthesize an event-triggered controller based solely on the available plant measurement. The event-triggering law that we construct is novel and only verified at periodic time instants, i.e., periodic event-triggering mechanism, which is more adapted to practical implementation. The proposed approach ensures a global asymptotic stability property for the closed-loop system under mild conditions. The overall model is developed as a hybrid dynamical system to truly describe the mixed continuous-time and discrete-time dynamics. The stability is studied using appropriate Lyapunov functions. The efficiency of the technique is illustrated on a numerical example.
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Paper Nr:	85
Title:	An Unsupervised Neural Network Approach for Solving the Optimal Power Flow Problem
Authors:	Alexander S. Marcial and Magnus Perninge
Abstract:	Optimal Power Flow is a central tool for power system operation and planning. Given the substantial rise in intermittent power and shorter time windows in electricity markets, there’s a need for fast and efficient solutions to the Optimal Power Flow problem. With this in consideration, this paper propose an unsupervised deep learning approach to approximate the optimal solution of Optimal Power Flow problems. Once trained, deep learning models benefit from being several orders of magnitude faster during inference compared to conventional non-linear solvers.
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Paper Nr:	88
Title:	Multi-Agent Pathfinding for Indoor Quadcopters: A Platform for Testing Planning-Acting Loop
Authors:	Matouš Kulhan and Pavel Surynek
Abstract:	We study the planning-acting loop for multi-agent path finding with continuous time (MAPF R ). The standard MAPF is a problem of navigating agents from their start positions to specified individual goal positions so that agents do not collide with each other. The standard MAPF takes place in a discrete graph with agents located in its vertices and instantaneous moves of agents across edges. MAPFR adds continuous elements to MAPF via allowing agents to wait in a vertex for arbitrary length of time to avoid the collision. We focus in this paper on executing MAPFR plans with a group of Crazyflies, small indoor quadcopters. We show how to modify the existing continuous-time conflict-based search algorithm (CCBS) for MAPF R to produce plans that are suitable for execution with the quadcopters. Our platform can be used for testing suitability of variants of MAPF for execution with real agents. Our finding is that the MAPF variant with continuous time and the related CCBS algorithm allows for extensions that can produce safe plans for quadcopters, namely cylindrical protection zone around each quadcopter can be introduced at the planning level.
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Paper Nr:	100
Title:	Fault Diagnosis with Stacked Sparse AutoEncoder for Multimode Process Monitoring
Authors:	Yahia Kourd, Messaoud Ramdani, Riadh Toumi and Ahmed Samet
Abstract:	Traditional process monitoring generally assumes that process data follow a Gaussian distribution with linear correlation. Nevertheless, this sort of restriction cannot be satisfied in reality since many industrial processes are nonlinear in nature. This work provides an enhanced multivariate statistical process monitoring technique based on the Stacked Sparse AutoEncoder and K-Nearest Neighbor (SSAE-KNN). This approach consists of developing a model by using Stacked Sparse AutoEncoder (SSAE) to get the residual space, which is the main tool in detecting and reconstructing the potential missing data by residual space. The monitoring statistics in this space are constructed using KNN rules; the threshold values for SSAE-KNN process monitoring are estimated utilizing the Kernel Density PDF Estimation (KDE) method, and an enhanced Sensor Validity Index (SVI) is proposed to detect faulty data based on the reconstruction approach. The experimental results using actual data from a photovoltaic power station connected at the site of OuedKebrit, located in north-eastern Algeria, reveal the effectiveness of the proposed scheme and show its capacity to detect and identify sensor failures.
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Paper Nr:	114
Title:	Emergency Meteorological Data Preparation for Artillery Operations
Authors:	Jan Ivan, Michal Šustr, David Sládek, Jaroslav Varecha and Jiří Gregor
Abstract:	The article discusses a research project focused on new approaches to the meteorological preparation of artillery units. As can be observed in the current conditions of the war in Ukraine, artillery is a key component of both warring parties. The effectiveness of artillery is based on the accuracy of its fire. However, in order for the artillery to fire accurately, it is necessary to compensate for all the influences that may affect the shell flight. The main component of influencing factors are meteorological conditions, which the artillery determines by upper air sounding of the atmosphere. However, currently used methods are very susceptible to enemy activity and artillery must therefore be able to obtain meteorological data at any level of degradation of its capabilities. This article describes the research project which is aimed to create an aggregated predictive model based on historical meteorological data. Using this model, it would be possible to obtain meteorological data autonomously, without the need for complex sounding of the atmosphere or obtaining data from external sources. The article describes the proposed approaches to the solution of the project and the creation of an aggregated predictive model for the use of artillery units.
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Paper Nr:	117
Title:	Spectral Clustering in Rule-Based Algorithms for Multi-Agent Path Finding
Authors:	Irene Saccani, Kristýna Janovská and Pavel Surynek
Abstract:	We focus on rule-based algorithms for multi-agent path finding (MAPF) in this paper. MAPF is a task of finding non-conflicting paths connecting agents’ specified initial and goal positions in a shared environment specified via an undirected graph. Rule-based algorithms use a fixed set of predefined primitives to move agents to their goal positions in a complete manner. We propose to apply spectral clustering on the underlying graph to decompose the graph into highly connected component and move agents to their goal cluster first before the rule-based algorithm is applied. The benefit of this approach is twofold: (1) the algorithms are often more efficient on highly connected clusters and (2) we can potentially run the algorithms in parallel on individual clusters.
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Paper Nr:	123
Title:	Creating of Minefield Breaches with Artillery
Authors:	Michal Švehlík, Michal Šustr, Ladislav Potužák, Jaroslav Varecha and Jan Drábek
Abstract:	This article describes research project about new approach to creating breaches in engineer obstacles by using artillery fire. In current russian-ukraine war can be observed massive use of explosive and non-explosive obstacles within position defence. Efficiency of attack of task forces is in this case directly influenced by their ability to overcome these obstacles. Main issue for the attacking force represents minefields which slow down and restrict manoeuvre and cause casualties. Breaches in minefields are created by units of combat engineers manually or by special mine clearing equipment. During that time is the unit threatened by the enemy, especially special engineering equipment is a priority target. The aim of the research is to propose and verify the possibility of using artillery as a mean to create breaches in engineer obstacles, especially in minefields, giving the attacking force alternative, contingency or emergency way of creating breach. The article introduces basic aspects of explosive obstacles and minefields, analyses tactical aspects of creating breaches in them and proposes possible approaches to solving the problematics by using artillery.
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Paper Nr:	128
Title:	CFRLI-IDM: A Counterfactual Risk Level Inference Based Intelligence Driver Model for Extremely Aggressive Cut-in Scenario in China
Authors:	Yongqiang Li, Yang Lv, Quan Wang and Qiankun Miao
Abstract:	When conducting unmanned delivery tasks on side roads in China, unmanned delivery vehicles sometimes face a dual challenge of aggressive cut-ins and reckless followers driving closely behind them. To address this challenge, we propose a cut-in response strategy named Counterfactual Risk Level Inference-based Intelligence Driver Model (CFRLI-IDM). The CFRLI-IDM method utilizes an improved Intelligent Driver Model (IDM) as the initial longitudinal control strategy for the ego vehicle. It then leverages counterfactual inference to construct an optimization problem, aiming to derive a longitudinal control strategy that satisfies the ego vehicle’s risk threshold constraint while maximizing compliance with the rear vehicle’s maximum acceptable braking deceleration constraint, with minimal changes to the initial strategy. To evaluate the effectiveness of our proposed method, we design an extremely challenging cut-in simulation scenario incorporating the aforementioned factors and validate the algorithm within this simulated environment. Experimental results demonstrate that our method prioritizes the safety of the ego vehicle while ensuring the safety of the rear vehicle as much as possible, substantially reducing the likelihood of safety accidents occurring in such complex scenarios.
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Paper Nr:	144
Title:	Distributed Predictive Control for Roundabout Crossing Modelled by Virtual Platooning
Authors:	Alessandro Bozzi, Simone Graffione, Roberto Sacile and Enrico Zero
Abstract:	Roundabouts pose complex challenges for autonomous vehicles. Approaching and crossing them safely requires a significant amount of information, much of which is typically unavailable. With autonomous vehicles becoming increasingly prevalent on the roads, new approaches are necessary to address these upcoming issues. While platoons and distributed control have been extensively studied in the past decade, roundabouts have received less attention. This paper presents a distributed Nonlinear Model Predictive Control (NMPC) approach using the Alternating Direction Method of Multipliers (ADMM) to utilize virtual platooning and enhance the throughput of a roundabout without requiring approaching vehicles to come to a stop. Instead, it manages the velocity of each vehicle while maintaining a safe distance. The proposed approach is validated through two case studies.
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Paper Nr:	147
Title:	Interval Type-2 Fuzzy Control to Solve Containment Problem of Multiple USV with Leader’s Formation Controller
Authors:	Wen-Jer Chang, Yann-Horng Lin and Cheung-Chieh Ku
Abstract:	An interval type-2 (IT2) fuzzy controller design method is proposed in this paper to simultaneously solve the formation and containment control problems of multi-unmanned surface vehicles (USVs) system. Via the construction of IT2 Takagi-Sugeno Fuzzy Model (IT2T-SFM), the control problem of nonlinear multi-USVs system can be transferred into the linear problem and the uncertain factors can be described more completely. Based on the IT2T-SFM, the IT2 fuzzy formation and containment controller is designed by the imperfect premise matching method to achieve the more flexible design process. When the IT2 fuzzy formation controller is designed for the leader USVs system, some problems are occurred in the containment analysis process. Therefore, the design concept for unknown leader’s input is extended to solve the problem. And a technique is applied to obtain the less-conservative IT2 fuzzy controller design process for the containment purpose. Finally, the simulation results are presented to verify the proposed design method.
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Paper Nr:	150
Title:	Offline Feature-Based Reinforcement Learning with Preprocessed Image Inputs for Liquid Pouring Control
Authors:	Stephan Pareigis, Jesus E. Hermosilla-Diaz, Jeeangh J. Reyes-Montiel, Fynn L. Maaß, Helen Haase, Maximilian Mang and Antonio Marin-Hernandez
Abstract:	A method for the creation of a liquid pouring controller is proposed, based on experimental data gathered from a small number of experiments. In a laboratory configuration, a UR5 robot arm equipped with a camera near the end effector holds a container. The camera captures the liquid pouring from the container as the robot adjusts its turning angles to achieve a specific pouring target volume. The proposed controller applies image analysis in a preprocessing stage to determine the liquid volume pouring from the container at each frame. This calculated volume, in conjunction with an estimated target volume in the receiving container, serves as input for a policy that computes the necessary turning angles for precise liquid pouring. The data received on the physical system is used as Monte-Carlo episodes for training an artificial neural network using a policy gradient method. Experiments with the proposed method are conducted using a simple simulation. Convergence proves to be fast and the achieved policy is independent of initial and goal volumes.
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Paper Nr:	154
Title:	Preliminary Results on Controllability of Serial Robot-Manipulators in Singular Configurations
Authors:	Mir Mamunuzzaman and Jörg Mareczek
Abstract:	We develop a standard system representation and analyse controllability properties for velocity kinematics of robot-manipulators located on singularities. These are positions where the Jacobian loses rank. Since its column vectors span the set of admissible workspace velocity directions, it is still a widespread misunderstanding that some directions would be locked on singularities and thus had to be bypassed as far as possible. We will show that this does not generally hold: On some types of singularities, the kinematic shows local redundancy, which can be used to generate paths crossing the singularity in any desired workspace velocity direction. To further analyse controllability properties, we develop an SVD-based method to represent the Jacobian-based velocity kinematics in standard system description of control theory without the need for inverse kinematics (IK). In many cases, IK do not offer a unique solution on singularities and, therefore, cannot be used. Furthermore, we present a modification of the SVD-based method for which the analytical calculation effort is feasible. The resulting system description has the advantage of being a simple decoupled set of single-integrators where the system states are divided into one set describing admissible workspace motions and a second set describing possible internal motions, also called nullspace motion. Based on this standard system representation, we determine local controllability and local accessibility for two different types of singularities. Finally, we illustrate our methods by means of a simple 3-DoF SCARA-type manipulator.
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Paper Nr:	155
Title:	A Meta-Review on the Use of Artificial Intelligence in the Context of Electrical Power Grid Operators
Authors:	Daniel Staegemann, Christian Haertel, Christian Daase, Matthias Pohl and Klaus Turowski
Abstract:	With the growing energy hunger of today’s society and the ongoing transition from fossil fuels to renewable energies, the demands on the electrical power grids are growing. Consequently, grid operators are seeking for ways to improve their performance, flexibility, and reliability. One of these avenues is the use of artificial intelligence. However, while there are already promising endeavors, this research stream is still far from being mature. For this reason, in the publication at hand, a meta-review is presented that outlines important themes, trends, and challenges to provide scientists interested in the domain with a starting point for new projects.
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Paper Nr:	159
Title:	Hand-Drawn Diagram Correction Using Machine Learning
Authors:	Tenga Yoshida and Hiroyuki Kobayashi
Abstract:	This paper introduces a real-time correction technique for hand-drawn diagrams on tablets, leveraging machine learning to mitigate inaccuracies caused by hand tremors. A novel fusion of classification and regression models is proposed; initially, the classification model discerns the geometric shape being drawn, aiding the regression model in making precise corrective predictions during the drawing process. Additionally, a unique Mean Angle of Vector (MAV) loss function is introduced to minimize angle changes in vectors formed by consecutive points, thereby reducing hand tremors especially in straight line segments. The MAV function not only facilitates real-time corrections but also preserves the drawing fluidity, enhancing user satisfaction. Experimental results highlight improved correction accuracy, particularly when employing classification alongside regression. However, the MAV function may round off sharp corners, indicating areas for further refinement. This work paves the way for more intuitive and user-friendly digital sketching and diagramming applications.
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Paper Nr:	177
Title:	University Recommendation System for Undergraduate Studies in Bangladesh Using Distributed Machine Learning
Authors:	Ahmed Nur Merag, Rezwana Chaudhury Raka, Sumya Afroj, Md Humaion Kabir Mehedi and Annajiat Alim Rasel
Abstract:	The study proposes a distributed machine learning-based university recommendation system (URS) in Bangladesh to help undergraduate students make informed decisions based on user ratings. The system uses advanced distributed machine learning models such as collaborative filtering and popularity-based recommender model which consists of KNNwithmeans model and singular value decomposition (SVD) model to process data and provide accurate recommendations, significantly enhancing the university selection process for students. This study advances educational technology and provides a useful tool for undergraduates in Bangladesh.
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Paper Nr:	6
Title:	A Concept for Optimizing Motor Control Parameters Using Bayesian Optimization
Authors:	Henning Cui, Markus Görlich-Bucher, Lukas Rosenbauer, Jörg Hähner and Daniel Gerber
Abstract:	Electrical motors need specific parametrizations to run in highly specialized use cases. However, finding such parametrizations may need a lot of time and expert knowledge. Furthermore, the task gets more complex as multiple optimization goals interplay. Thus, we propose a novel approach using Bayesian Optimization to find optimal configuration parameters for an electric motor. In addition, a multi-objective problem is present as two different and competing objectives must be optimized. At first, the motor must reach a desired revolution per minute as fast as possible. Afterwards, it must be able to continue running without fluctuating currents. For this task, we utilize Bayesian Optimization to optimize parameters. In addition, the evolutionary algorithm NSGA-II is used for the multi-objective setting, as NSGA-II is able to find an optimal pareto front. Our approach is evaluated using three different motors mounted to a test bench. Depending on the motor, we are able to find good parameters in about 60-100%.
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Paper Nr:	21
Title:	On Selecting Optimal Hyperparameters for Reinforcement Learning Based Robotics Applications: A Practical Approach
Authors:	Ignacio Fidalgo, Guillermo Villate, Alberto Tellaeche and Juan I. Vázquez
Abstract:	Artificial intelligence (AI) is increasingly present in industrial applications and, in particular, in advanced robotics, both industrial and mobile. The main problem of these type of applications is that they use complex AI algorithms, in which it is necessary to establish numerous hyperparameters to achieve an effective training of the same. In this research, we introduce a pioneering approach to reinforcement learning in the realm of industrial robotics, specifically targeting the UR3 robot. By integrating advanced techniques like Deep Q-Learning and Proximal Policy Optimization, we’ve crafted a unique motion planning framework. A standout novelty lies in our application of the Optuna library for hyperparameter optimization, which, while not necessarily enhancing the robot’s end performance, significantly accelerates the convergence to the optimal policy. This swift convergence, combined with our comprehensive analysis of hyperparameters, not only streamlines the training process but also paves the way for efficient real-world robotic applications. Our work represents a blend of theoretical insights and practical tools, offering a fresh perspective in the dynamic field of robotics.
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Paper Nr:	23
Title:	Fractional Order-Sliding-Mode Controller for Regulation of a Nonlinear Chemical Process with Variable Delay
Authors:	Antonio Di Teodoro, Marco Herrera and Oscar Camacho
Abstract:	The present work shows the application of a new controller based on combining the fractional order calculus concepts with the sliding mode theory to a non-linear system with variable delay. The power of fractional-order calculus is used to identify the real process and represent it as a reduced-order model. From this model, the controller is developed using the sliding-mode control procedure. An SMC based on FOPDT and one based on fractional calculus are compared using some performance indicators to assess performance quantitatively.
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Paper Nr:	47
Title:	A Clustering-Based Approach for Adaptive Control Applied to a Hybrid Electric Vehicle
Authors:	Rian Beck, Sudarsan K. Venkatesan, Joram Meskens, Jeroen Willems, Edward Kikken and Bruno Depraetere
Abstract:	In this paper we present an approach to adapt the parameters of controllers during operation. It is targeted at industrial adoption, relying on controllers of the same type currently in use, but adjusting their gains at run-time based on varying system and / or environment conditions. As the key contribution of this paper we present a method to discover what condition variations warrant a control adaptation for cases where this is not known up front. The goal is not to achieve a better performance than other adaptive control schemes, but to provide a different method of designing or deciding how to build adaptation logic. To achieve this we use data-driven methods to, in an offline preprocessing step: (I) derive features that quantify system / environment variations, (II) optimize the control parameters for the distinct feature values, (III) search for clusters in the multi-dimensional space of both these features and control parameters, looking for sets of similar features as well as control parameters to be used. Once a set of clusters is defined, an online adaptive controller is then synthesized by (I) building a classifier to determine which cluster the currently observed conditions belong to, and (II) selecting the optimal control parameters for that cluster. This paper provides a first illustration of the method, without theoretical analysis, on an example case of energy management for a hybrid electrical vehicle, for which an Equivalent Consumption Minimization Strategy controller is built whose parameters are adjusted as the detected cluster changes. The results show an increase in energy-efficiency of the adaptive control method over the non-adaptive one in a variety of scenarios.
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Paper Nr:	89
Title:	Trajectory Planning for Multiple Vehicles Using Motion Primitives: A Moving Horizon Approach Under Uncertainty
Authors:	Bahaaeldin Elsayed and Rolf Findeisen
Abstract:	Planning motion in cluttered and uncertain environments for autonomous systems remains a daunting challenge, especially when prioritizing safety and efficiency. This paper introduces an innovative method of melding motion primitives with a moving horizon strategy, drawing on the principles of model predictive control. Using motion primitives, we achieve simplified, high-level depictions of vehicle movement using various linear time-invariant models for each mode. This significantly cuts computational complexity in the subsequent planning stages. We integrate constraint backoff based on system uncertainties to ensure that generated trajectories are robust, collision-free, and adhere to all necessary constraints. This comprehensive framework produces optimal, safe trajectories that cater to environmental uncertainties and are suitable for real-time applications. Our simulation outcomes robustly highlight the strengths and distinctiveness of the proposed approach.
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Paper Nr:	113
Title:	Hand Gesture Interface to Teach an Industrial Robots
Authors:	Mojtaba A. Khanesar and David Branson
Abstract:	The present paper proposes user gesture recognition to control industrial robots. To recognize hand gestures, MediaPipe software package and an RGB camera is used. The proposed communication approach is an easy and reliable approach to provide commands for industrial robots. The landmarks which are extracted by MediaPipe software package are used as the input to a gesture recognition software to detect hand gestures. Five different hand gestures are recognized by the proposed machine learning approach in this paper. Hand gestures are then translated to movement directions for the industrial robot. The corresponding joint angle updates are generated using damped least squares inverse kinematic approach to move the industrial robot in a plane. The motion behaviour of the industrial robot is simulated within V-REP simulation environment. It is observed that the hand gestures are communicated with high accuracy to the industrial robot and the industrial robot follows the movements accurately.
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Paper Nr:	133
Title:	Mobile Robot Navigation Based on Pedestrian Flow Model Considering Human Unsteady Dynamic Behavior
Authors:	Ryusei Shigemoto and Ryosuke Tasaki
Abstract:	Achieving robot navigation that satisfies the requirements of safety and efficiency in a dynamic environment crowded with people is a challenging task because of the need to implement social aspects of robot behavior. In this study, a robot navigation method based on an unsteady dynamic pedestrian flow model is proposed, taking into account the unsteady dynamic nature of pedestrian flow, which has not been taken into account in conventional algorithms. We propose a method that enables continuous following of unsteady pedestrian flow, which allow the robot to approach the destination safely and efficiently. The social compatibility of the proposed navigation system, consisting of safety and efficiency, is evaluated through several simulations and actual experiments.
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Paper Nr:	135
Title:	A Study on Acquisition of 3D Self-Localization by Fluorescent Lights
Authors:	Rikuto Ozawa and Hiroyuki Kobayashi
Abstract:	The authors proposed a method called “CEPHEID” in previous study. This method utilizes individual differences in power spectra obtained from illumination lights to identify individuals, allowing for self-location estimation using lighting fixtures embedded in the ceiling as landmarks. However, the information obtainable through this method is limited to a two-dimensional plane. To overcome this limitation, in this study, we introduced a regression model in addition to the deep learning model used for individual identification. The regression model aims to estimate the distance to the illumination light, enabling the acquisition of self-position information in three dimensions. This paper presents the evaluation of the accuracy of the regression model’s distance estimation.
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Paper Nr:	136
Title:	Decentralized Federated Learning Architecture for Networked Microgrids
Authors:	Ilyes Naidji, Chams E. Choucha and Mohamed Ramdani
Abstract:	The expansion of large-scale distributed renewable energy drives the emergence of networked microgrids systems, necessitating the development of an efficient energy management approach to minimize costs and maintain energy self-sufficiency. The use of smart systems that are based on deep learning algorithms has be-come prevalent while addressing the energy management problem due to its real-time scheduling capabilities. However, training deep-learning algorithms requires substantial energy operation data from these microgrids, which raises concerns regarding privacy and data security when collecting data from various microgrids. To address this challenging problem, this article proposes a decentralized federated learning architecture for networked microgrids. The architecture incorporates a distributed federated learning (FL) mechanism to guarantee data privacy and security and prevent the system from signle point of failure. A decentralized networked microgrids model is constructed, where each participating microgrid has an energy management system responsible for managing its energy. The goal of the EMS is to minimize economic costs and maintain energy self-sufficiency. Initially, MGs independently undergo self-training using local energy operation data to train their individual models. Subsequently, these local models are regularly exchanged, and their parameters are aggregated to create a global model. This approach allows sharing of experiences among the microgrids without transmitting energy operation data, thereby safeguarding privacy and ensuring data security and preventing from single point of failure.
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Paper Nr:	148
Title:	A Study on the Energy Efficiency of Various Gaits for Quadruped Robots: Generation and Evaluation
Authors:	Roman Zashchitin and Dmitrii Dobriborsci
Abstract:	This paper presents an approach for generating various types of gaits for quadrupedal robots using limb contact sequencing. The aim of this research is to explore the capabilities of reinforcement learning in reproducing and optimizing locomotion patterns. The proposed method utilizes the PPO algorithm, which offers improved performance and ease of implementation. By specifying a sequence of limb contacts with the ground, gaits such as Canter, Half bound, Pace, Rotary gallop, and Trot are generated. The analysis includes evaluating the energy efficiency and stability of the generated gaits. The results demonstrate energy-efficient locomotion patterns and the ability to maintain stability. The findings of this study have significant implications for the practical application of legged robots in various domains, including inspection, construction, elderly care, and home security. Overall, this research showcases the potential of reinforcement learning in gait generation and highlights the importance of energy efficiency and stability in legged robot locomotion.
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Paper Nr:	158
Title:	Proposal of a New Approach Using Deep Learning for QR Code Embedding
Authors:	Kanaru Kumabuchi and Hiroyuki Kobayashi
Abstract:	The purpose of this research is to enhance the technique of embedding QR codes into arbitrary images using deep learning. Previous approaches faced the issue of compromising the quality when embedding QR codes into arbitrary images. We address this problem by proposing a deep learning model and learning method that can improve the quality of embedded images and accurately recover QR codes. Specifically, we design a new model using deep learning that embeds QR codes into images while minimizing the degradation of image quality. The effectiveness of the proposed model and learning method is validated through experiments, demonstrating the enhancement of image quality in the embedded images and accurate QR code recovery.
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Area 3 - Robotics and Automation

Full Papers

Paper Nr:	14
Title:	Hanging Drone: An Approach to UAV Landing for Monitoring
Authors:	Alan Kunz Cechinel, Juha Röning, Antti Tikanmaki, Edson R. DePieri and Patricia Della Méa Plentz
Abstract:	Wildfire has been an environmental, economic, and health problem worldwide. Technological advances have led to the popularization of Unmanned Aerial Vehicles (UAVs) for personal and business use. One of the Unmanned Aerial Vehicle (UAV) applications is monitoring. However, UAVs still have payload and battery limitations. UAVs can be an ally for wildfire management, but their use is challenging considering their restraints and the large size of monitored areas. Therefore, it is necessary to develop approaches to circumvent UAV limitations. This work’s approach allows a drone to land in strategic locations for data acquisition, resulting in significantly less battery consumption. The method uses principles from stereo vision through a monocular camera motion to estimate the relative position of a selected landing site, allowing a drone to hang itself by a hook in an artificial (e.g., aluminum frame, power line) or natural (e.g., tree branch) location. However, the system is limited to static landing sites where the FAST feature detector algorithm can detect features. The results showed that the landing site estimation system achieves over 90% accuracy in controlled scenarios. Moreover, the Landing Site Estimation System (LSES) allied with navigation controllers achieved 95% success in landing attempts with light and wind under control.
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Paper Nr:	15
Title:	CASP: Computer Aided Specimen Placement for Robot-Based Component Testing
Authors:	Julian Hanke, Matthias Stueben, Christian Eymüller, Maximilian E. Müller, Alexander Poeppel and Wolfgang Reif
Abstract:	The manufacturing industry is undergoing a significant transformation in the context of Industry 4.0, and production is shifting from mass products to individual products of batch size one. Moreover, the increasing complexity of components, e.g., due to additive manufacturing, makes the testing setups of components even more complex. Due to the low quantities of the components, it is not profitable to build test benches for each individual component to test a large number of different forces and torsions to ensure the needed product quality. In order to be able to test various components flexibly through different motions, we developed a concept to perform robot-based destructive component testing with industrial robots. The six degrees of freedom and the broad working range of an industrial robot make it possible to apply forces and torques to different products. Since industrial robots cannot apply the same forces and torques in all axis positions, a position must be calculated where the specimen can be tested. Therefore, we propose an approach for automatic specimen placement, which includes a format to map applicable forces and torques of industrial robots. Furthermore, we present an algorithmic approach to execute an automatic feasibility check for the required test motions and an automatic specimen placement using an exemplary robot-based component testing bench.
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Paper Nr:	27
Title:	Computing the Traversability of the Environment by Means of Sparse Convolutional 3D Neural Networks
Authors:	Antonio Santo, Arturo Gil, David Valiente, Mónica Ballesta and Adrián Peidró
Abstract:	The correct assessment of the environment in terms of traversability is strictly necessary during the navigation task in autonomous mobile robots. In particular, navigating along unknown, natural and unstructured environments requires techniques to select which areas can be traversed by the robot. In order to increase the autonomy of the system’s decisions, this paper proposes a method for the evaluation of 3D point clouds obtained by a LiDAR sensor in order to obtain the transitable areas, both in road and natural environments. Specifically, a trained sparse encoder-decoder configuration with rotation invariant features is proposed to replicate the input data by associating to each point the learned traversability features. Experimental results show the robustness and effectiveness of the proposed method in outdoor environments, improving the results of other approaches.
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Paper Nr:	31
Title:	Experimental Validation of an Actor-Critic Model Predictive Force Controller for Robot-Environment Interaction Tasks
Authors:	Alessandro Pozzi, Luca Puricelli, Vincenzo Petrone, Enrico Ferrentino, Pasquale Chiacchio, Francesco Braghin and Loris Roveda
Abstract:	In industrial settings, robots are typically employed to accurately track a reference force to exert on the surrounding environment to complete interaction tasks. Interaction controllers are typically used to achieve this goal. Still, they either require manual tuning, which demands a significant amount of time, or exact modeling of the environment the robot will interact with, thus possibly failing during the actual application. A significant advancement in this area would be a high-performance force controller that does not need operator calibration and is quick to be deployed in any scenario. With this aim, this paper proposes an Actor-Critic Model Predictive Force Controller (ACMPFC), which outputs the optimal setpoint to follow in order to guarantee force tracking, computed by continuously trained neural networks. This strategy is an extension of a reinforcement learning-based one, born in the context of human-robot collaboration, suitably adapted to robot-environment interaction. We validate the ACMPFC in a real-case scenario featuring a Franka Emika Panda robot. Compared with a base force controller and a learning-based approach, the proposed controller yields a reduction of the force tracking MSE, attaining fast convergence: with respect to the base force controller, ACMPFC reduces the MSE by a factor of 4.35.
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Paper Nr:	37
Title:	Robot Path Planning with Safety Zones
Authors:	Evis Plaku, Arben Çela and Erion Plaku
Abstract:	Path planning is essential for guiding a robot to its destination while avoiding obstacles. In practical scenarios, the robot is often required to remain within predefined safe areas during navigation. This allows the robot to divert from its main path during emergencies and follow alternative routes to a safety center. This paper introduces a novel method to incorporate safety zones into path planning. Each zone is defined by a central point and a radius. Our approach efficiently plans paths to the goal, ensuring that the robot can reach a safety center without having to travel more than the radius of the safety zone. Using sampling, our approach constructs a roadmap with navigation routes and identifies safe locations that satisfy the distance requirements for reaching a safety center. The safe portion of the roadmap is then searched to find a path to the goal. We demonstrate the effectiveness of our approach through simulated experiments in obstacle-rich 2D and 3D environments, utilizing car and blimp robot models.
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Paper Nr:	58
Title:	Comparative Analysis of Segmentation Techniques for Reticular Structures
Authors:	Francisco J. Soler, Luis M. Jiménez, David Valiente, Luis Payá and Óscar Reinoso
Abstract:	Nowadays neural networks are widely used for segmentation tasks and there is a belief that these approaches are synonymous of advances and improvements. This article aims to compare the performance of a neural network, trained in our previous work, and an algorithm which is specifically designed for the segmentation of reticular structures. As shown in this paper, in certain cases it is feasible to use conventional techniques outside the paradigm of artificial intelligence achieving the same performance. To prove this, in this article a quantitative and qualitative comparative analysis is carried out between an ad hoc algorithm for segmenting reticular structures and the model of neural network that provided the best results in our previous work in this task. Established techniques such as Random Sample Consensus (RANSAC) and region growing have been used to implement the proposed algorithm. For the quantitative analysis, standard metrics such as precision, recall and f1-score are used. These metrics will be calculated with a self-generated dataset, consisting of a thousand point clouds that were generated automatically in the previous work. The studied algorithm is tailor-made for this database. For reproducibility, code and datasets are provided at https://github.com/Urwik/ rrss grnd filter.git.
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Paper Nr:	75
Title:	Learning-Based Inverse Dynamic Controller for Throwing Tasks with a Soft Robotic Arm
Authors:	Diego Bianchi, Michele G. Antonelli, Cecilia Laschi, Angelo M. Sabatini and Egidio Falotico
Abstract:	Controlling a soft robot poses a challenge due to its mechanical characteristics. Although the manufacturing process is well-established, there are still shortcomings in their control, which often limits them to static tasks. In this study, we aim to address some of these limitations by introducing a neural network-based controller specifically designed for the throwing task using a soft robotic arm. Drawing inspiration from previous research, we have devised a method for controlling the movement of the soft robotic arm during the ballistic task. By employing a feed-forward neural network, we approximate the relationship between the actuation pattern and the resulting landing position. This enables us to predict the input sequence that needs to be transmitted to the robot’s actuators based on the desired landing coordinates. To validate our approach, we conducted experiments using a 2-module soft robotic arm, which was utilized to throw four different objects towards ten target boxes positioned beneath the robot. We considered two actuation modalities, depending on whether the distal module was activated. The results indicate a success rate, defined as the proportion of successful trials out of the total number of throws, of up to 68% when a single module was actuated. These findings demonstrate the potential of our proposed controller in achieving successful performance of the throwing task using a soft robotic arm.
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Paper Nr:	81
Title:	Curved Surface Inspection by a Climbing Robot: Path Planning Approach for Aircraft Applications
Authors:	Silya Achat, Julien Marzat and Julien Moras
Abstract:	This paper presents a path planning method for a climbing robot used for the exterior inspection of an aircraft. The objective is to plan a covering path with the least possible overlap, while respecting constraints related to an embedded sensor, a power cable, and the robot mechanical efforts. To achieve this, a semantic 2D grid model of the aircraft is first created by unfolding and labeling a 3D mesh reference model. An obstacle-based area decomposition method is then applied to divide this 2D discrete space into inspection areas. Inspection segments that satisfy the sensor constraints are then sampled and rearranged based on the order of the areas. The connections between the inspection segments are finally determined by a weighted A ∗ search approach, so as to limit the gravity-induced mechanical efforts on the robot.
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Paper Nr:	94
Title:	A PLF-CACC Design with Robustness to Communication Delays
Authors:	Khadir L. Besseghieur, Abdelkrim Nemra and Fethi Demim
Abstract:	In this paper, a new controller that makes a platoon of vehicles robust to large delays and loss of communication is proposed. The constant time headway spacing policy is adopted for the separations while the vehicles are allowed to exchange data according to the PLF communication pattern. Based on the SMC technique, the designed controller draw the platoon towards achieving the following and the string stability objectives. Semi strict L2 string stability is proved to be achieved in this two-vehicle look ahead strategy with the propose controller. Simulation are run in order to confirm the theoretical findings and to assess the effectiveness of the proposed controller. The performances in terms of string stability and robustness against delays are compared to a baseline PLF-CACC from the literature.
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Paper Nr:	98
Title:	SMaNa: Semantic Mapping and Navigation Architecture for Autonomous Robots
Authors:	Quentin Serdel, Julien Marzat and Julien Moras
Abstract:	Motivated by recent advances in machine learning applied to semantic segmentation, online 3D mapping is being extended to integrate semantic data. As these developments pave the way to the improvement of many robotic functionalities, the application of semantic mapping for navigation tasks remains to be further explored. In this paper we present an online Semantic Mapping and Navigation ROS architecture (SMaNa), with autonomous exploration as an application example. It is intended to be generic, so as to exploit state-of-the-art semantic mapping methods for unstructured environment and adapt them to perform jointly with a navigation graph builder and a semantic-aware A* path planner. The adequacy of multiple semantic mapping solutions for robot navigation in open environment and the performances of the architecture given the influence of localisation and semantic labelling uncertainty are evaluated in a closed-loop Ignition Gazebo simulation built from the 3DRMS synthetic dataset, and on the outdoor RELLIS-3D dataset.
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Paper Nr:	105
Title:	Design and Control of a Novel High Payload Light Arm for Heavy Aerial Manipulation Tasks
Authors:	Michele Marolla, Jonathan Cacace and Vincenzo Lippiello
Abstract:	Aerial manipulation is a rapidly emerging research field that explores the use of Unmanned Aerial Vehicles as mobile manipulators. To enable aerial manipulation, UAVs must be equipped with lightweight robotic arms capable of interacting with the environment. However, due to battery life constraints and payload limitations, these arms must be designed to be as light as possible, which restricts their ability to transport and manipulate heavy objects. In this work, we introduce a novel aerial manipulator prototype designed specifically for high payload manipulation. The arm is designed to have its center of mass as close as possible to its base, which is attached to the aerial frame. The arm incorporates a system of belts to facilitate the movement of its various joints. This paper presents the arm’s design, along with a control approach to compensate for the elasticity introduced by the belts. To showcase the system’s capabilities, we conduct two sets of experiments. Firstly, the arm is tested within a controlled laboratory environment. Secondly, we deploy an aerial robot equipped with the proposed prototype in a powerline maintenance task.
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Paper Nr:	115
Title:	Learning Based Interpretable End-to-End Control Using Camera Images
Authors:	Sandesh A. Hiremath, Praveen K. Gummadi, Argtim Tika, Petrit Rama and Naim Bajcinca
Abstract:	This work proposes a learning-based controller for an autonomous vehicle to follow lanes on highways and motorways. The controller is designed as an interpretable deep neural network (DNN) that takes as input only a single image from the front-facing camera of an autonomous vehicle. To this end, we first implement an image-based model predictive controller (MPC) using a DNN, which takes as input 2D coordinates of the reference path made available as image pixels coordinates. Consequently, the DNN based controller can be seamlessly integrated with the perception and planner network to finally yield an end-to-end interpretable learning-based controller. Here, all of the controller components, namely- perception, planner, state estimation, and control synthesizer, are differentiable and thus capable of active and event-triggered adaptive training of the relevant components. The implemented network is tested in the CARLA simulation framework and then deployed in a real vehicle to finally demonstrate and validate its performance.
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Paper Nr:	122
Title:	Position/Velocity Aided Leveling Loop: Continuous-Discrete Time State Multiplicative-Noise Filter Case
Authors:	Irina Avital, Isaac Yaesh and Adrian-Mihail Stoica
Abstract:	The problem of leveling using a low cost Inertial Measurement Unit (IMU) is considered, where the IMU measurements are corrupted with white noise. In such a case the state equations are subject to state-multiplicative noise. To cope with this noise, a state-Multiplicative Kalman Filter (MKF) is applied. The state components for the Kalman filter implementation include the Body Position Vector (BPV), the Body Velocity Vector (BVV), which is just the Ground Velocities Vector (GVV), projected onto the body axes and the three direction cosines related to the roll and pitch angles. The BVB is assumed to be measured using a Doppler Velocity Log (DVL) device which consists of four antennas measuring the Doppler effect. Similarly, it is assumed that the corresponding BPV can be measured, for instance, using the received signal power at those four antennas. The paper includes numerical simulations and implementation aspects related to the sampled data nature of the estimation problem.
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Paper Nr:	161
Title:	Learning How to Use a Supernumerary Thumb
Authors:	Ali S. Kaplan, Emre A. Ödemiş, Emre Doğan, Mehmet O. Yıldırım, Youness Lahdili, Amr Okasha and Kutluk B. Arıkan
Abstract:	This study presents a novel system consisting of a supernumerary robotic thumb and a virtual reality-based mirror paradigm in a leader-follower mode. As the extra thumb skeleton, a planar robotic mechanism with two degrees of freedom is utilized. The experimental setup poses the task of acquiring proficiency in controlling the supernumerary second thumb throughout a five-day duration of engaging in the leader-follower game. There is evidence that after five days of practice, a subject’s motor performance improves and motor variability decreases.
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Paper Nr:	162
Title:	Thorough Analysis and Reasoning of Environmental Factors on End-to-End Driving in Pedestrian Zones
Authors:	Qazi H. Jan, Arshil A. Khan and Karsten Berns
Abstract:	With the development of machine learning techniques and increase in their precision, they are used in different aspects of autonomous driving. One application is end-to-end driving. This approach directly takes in the sensor data and outputs the control value of the vehicle. End-to-end systems have widely been used. The goal of this work is to investigate the effect of change in weather condition, presence of pedestrians, and reason the prediction failure, along with improving the results in a pedestrian zone. Driving through the pedestrian zone is challenging due to the narrow path and crowd of people. This work uses RGB images from a front-facing camera mounted on the roof of a minibus and outputs the steering angle of the vehicle. A Convolutional Neural Network (CNN) is implemented for regression prediction. The testing was first done in a simulation environment which comprised of the replicated version of the campus, the sensor system and the vehicle model. Thorough testing is done in different weather conditions and with the simulated pedestrians to check the robustness of the system for such diversified changes in the environment. The vehicle avoided the simulated pedestrians placed randomly at the boundary of narrow paths. In an unseen environment, the vehicle approached the region with the same texture it was trained on. Later, the system was transferred to a real machine and further trained and tested. Due to unavailability of the ground truth, the results can not be delineated for real world testing, but are reasoned through visual monitoring. The vehicle followed the path and performed well in an unseen environment as well.
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Paper Nr:	166
Title:	High-Velocity Walk-Through Programming for Industrial Applications: A Safety-Oriented Approach
Authors:	Simone di Napoli, Mattia Bertuletti, Mattia Gambazza, Matteo Ragaglia, Cesare Fantuzzi and Federica Ferraguti
Abstract:	Traditionally, industrial robots are programmed by highly specialized workers that either directly write code in platform-specific languages, or use dedicated hardware (teach-pendant) to move the robot through the desired via-points. Unsurprisingly, the inherently complex and time-consuming nature of this task is one of the factors that are still preventing industrial manipulators from being massively adopted by companies that require a high degree of flexibility in order to cope with limited production volumes and rapidly changing product requirements. In this context, the introduction of sensor-based walk-through programming approaches represents the ideal solution as far as the need to reduce programming complexity and time is concerned. Nevertheless, one of the typical shortcomings of these solutions consists in limited reachable velocities during the programming phase due to safety constraints. To this regard, this paper proposes a safety architecture for walk-through programming of industrial manipulators specifically designed in order to reach high velocities while guaranteeing the operator’s safety. The proposed solution is validated on an industrial manipulator.
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Paper Nr:	174
Title:	2D LiDAR-Based Human Pose Tracking for a Mobile Robot
Authors:	Zhenyu Gao, Ze Wang, Ludovic Saint-Bauzel and Faïz Ben Amar
Abstract:	Human pose tracking is a practical feature for service robots, which allows the robot to predict the user’s trajectory and behavior and thus provide appropriate assistance for them. In this paper, we propose a human pose tracking method based on a knee-high 2D LiDAR mounted on the mobile robot. Inspired by human gait, a motion intention zoning, and a walking gait model are proposed to adapt to various motion patterns and achieve accurate orientation estimation. We propose a Kalman Filter-based human pose tracker that considers the leg occlusion problem and the data association of legs. We evaluate the proposed method’s performance in various complex scenarios and demonstrate robustness to leg occlusion. We released our implementation as open-source code∗ .
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Paper Nr:	187
Title:	From Point Cloud Perception Toward People Detection
Authors:	Assia Belbachir, Antonio M. Ortiz, Atle Aalerud and Ahmed N. Belbachir
Abstract:	Point clouds have become significant data inputs for 3D representation, enabling accurate analysis of 3D scenes and objects. People detection from point clouds is a challenging task due to data sparsity, irregularity, occlusion, and real-time detection constraints. Existing methods based on handcrafted features or deep learning have limitations in handling occlusions, pose variations, and fast detection. This paper introduces a Random Forest classifier for people detection in point clouds, aiming to achieve both accuracy and fast performance. The point cloud data are acquired using a multi-point LiDAR system. First experiments demonstrate the effectiveness of the approach and its efficient detection compared to Multiple Layer Perceptron (MLP) in our collected Dataset.
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Short Papers

Paper Nr:	10
Title:	Advanced Trajectory Planning and 3D Waypoints Navigation of Unmanned Underwater Vehicles Based Fuzzy Logic Control with LOS Guidance Technique
Authors:	Fethi Demim, Hadjira Belaidi, Abdenebi Rouigueb, Ali Z. Messaoui, Kahina Louadj, Sofian Saghour, Mohamed A. Benatia, Mohamed Chergui, Abdelkrim Nemra, Ahmed Allam and Elhaouari Kobzili
Abstract:	Trajectory planning is a critical action for achieving the objectives of Unmanned Underwater Vehicles (UUVs). To navigate through complex environments, this study investigates motion trajectory planning using Rapidly-exploring Random Trees (RRT) and Fuzzy Logic Control (FLC). Our goal is to explore the use of the RRT trajectory planning algorithm to generate waypoints in a known static environment. In this case, the UUV’s planned trajectory can meet the required conditions for obstacle avoidance. By using various objective functions, the model can be solved, and the corresponding control variables can be adjusted to effectively accomplish the requirements of underwater navigation. This technique has been successfully applied in various experimental scenarios, demonstrating the effectiveness of the FLC regulator. For instance, The 3D waypoint navigation challenge has been tackled by implementing the Fuzzy Controller, which utilizes the robust Line-Of-Sight (LOS) guidance technique. Experimental results demonstrate that the FLC regulator efficiently navigates through the waypoints, maintains an accurate course, controls the pitch and yaw angles of the UUV, and successfully reaches the final destination.
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Paper Nr:	12
Title:	RoboToy Demoulding: Robotic Demoulding System for Toy Manufacturing Industry
Authors:	Daniel Sánchez-Martínez, Carlos A. Jara and Francisco Gomez-Donoso
Abstract:	Industrial environments and product manufacturing processes are currently being automated and robotized. Nowadays, it is common to have robots integrated in the automotive industry, robots palletizing in the food industry and robots performing welding tasks in the metal industry. However, there are many traditional and manual sectors out of date with technology, such as the toy manufacturing industry. This work describes a new robotic system able to perform the demoulding task in a toy manufacturing process, which is a tedious labor-intensive and potentially hazardous task for human operators. The system is composed of specialised machinery about the rotational moulding manufacturing process, cameras, actuators, and a collaborative robot. A vision-based algorithm makes this system capable of handling soft plastic pieces which are deformable and flexible during demoulding. The system reduces the stress and potential injuries to human operators, allowing them to perform other tasks with higher dexterity requirements or relocate to other sub-tasks of the process where the physical effort is minor.
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Paper Nr:	24
Title:	TEAM: A Parameter-Free Algorithm to Teach Collaborative Robots Motions from User Demonstrations
Authors:	Lorenzo Panchetti, Jianhao Zheng, Mohamed Bouri and Malcolm Mielle
Abstract:	Learning from demonstrations (LfD) enables humans to easily teach collaborative robots (cobots) new motions that can be generalized to new task configurations without retraining. However, state-of-the-art LfD methods require manually tuning intrinsic parameters and have rarely been used in industrial contexts without experts. We propose a parameter-free LfD method based on probabilistic movement primitives, where parameters are determined using Jensen-Shannon divergence and Bayesian optimization, and users do not have to perform manual parameter tuning. The cobot’s precision in reproducing learned motions, and its ease of teaching and use by non-expert users are evaluated in two field tests. In the first field test, the cobot works on elevator door maintenance. In the second test, three factory workers teach the cobot tasks useful for their daily workflow. Errors between the cobot and target joint angles are insignificant—at worst 0.28 deg—and the motion is accurately reproduced—GMCC score of 1. Questionnaires completed by the workers highlighted the method’s ease of use and the accuracy of the reproduced motion. Public implementation of our method and datasets are made available online.
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Paper Nr:	26
Title:	Driver Attention Estimation Based on Temporal Sequence Classification of Distracting Contexts
Authors:	Raluca D. Brehar, George Coblişan, Attila Füzes and Radu Dănescu
Abstract:	A framework for distracted driving level or the degree of attention which a driver pays to the act of driving, is presented in this paper. It uses visual based action recognition models applied on color images that capture the driver’s face and hands. The proposed approach contains a temporal sequence model that aggregates information from two object detectors which recognize distracting contexts generated by (1) distracting objects that appear in the images such as mobile devices and (2) the face orientation of the driver, the hands and their position with respect to the wheel. The driver’s attention score is predicted using the temporal sequence classification model, a long short term memory, that considers time series features computed based on object detection information.
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Paper Nr:	32
Title:	Evaluating Deep Learning Assisted Automated Aquaculture Net Pens Inspection Using ROV
Authors:	Waseem Akram, Muhayyuddin Ahmed, Lakmal Seneviratne and Irfan Hussain
Abstract:	In marine aquaculture, inspecting sea cages is an essential activity for managing both the facilities’ environmental impact and the quality of the fish development process. Fish escape from fish farms into the open sea due to net damage, which can result in significant financial losses and compromise the nearby marine ecosystem. The traditional inspection system in use relies on visual inspection by expert divers or Remotely Operated Vehicles (ROVs), which is not only laborious, time-consuming, and inaccurate but also largely dependent on the level of knowledge of the operator and has a poor degree of verifiability. This article presents a robotic-based automatic net defect detection system for aquaculture net pens oriented to on-ROV processing and real-time detection. The proposed system takes a video stream from an onboard camera of the ROV, employs a deep learning detector, and segments the defective part of the image from the background under different underwater conditions. The system was first tested using a set of collected images for comparison with the state-of-the-art approaches and then using the ROV inspection sequences to evaluate its effectiveness in real-world scenarios. Results show that our approach presents high levels of accuracy even for adverse scenarios and is adequate for real-time processing on embedded platforms.
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Paper Nr:	34
Title:	Single Source of Truth: Integrated Process Control and Data Acquisition System for the Development of Resistance Welding of CFRP Parts
Authors:	Michael Vistein, Monika Mayer, Manuel Endraß and Frederic Fischer
Abstract:	For the development of a novel (industrial) process, in particular within a research environment, a very flexible and adjustable control and data acquisition system is required. Traditional SCADA systems often are not designed for frequent changes. To facilitate the development process, the storage of all relevant data from process parameters to measurement data at a single location, as a “single-source-of-truth” is desirable. This paper introduces an integrated process control and data acquisition system that is built around the open-source central data storage system “shepard” which facilitates the evaluation of the process and offers potential for inline-optimization. The system is evaluated by the example of the production of a thermoplastic component. As part of the European Clean Sky II large passenger aircraft project, the German Aerospace Center produces the 8-meter long upper shell of the multifunctional fuselage demonstrator (MFFD) made from thermoplastic composites. The frames are attached to the skin by resistance welding, which is done using an actuated gantry system. This novel process has the potential to disrupt standard aircraft assembly by dust-less welding in a fully automated yet interactively customizable manner which is hence relevant for every process development context.
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Paper Nr:	41
Title:	Experimental Validation of the Non-Orthogonal Serret-Frenet Parametrization Applied to the Path Following Task
Authors:	Filip Dyba
Abstract:	The path following task belongs to the fundamental robotic tasks. It consists of following a spatial curve parametrized with a curvilinear distance. In this time-independent approach no time regimes are imposed on a robot. In fact, it is a natural definition of a task for many robots, e.g. autonomous vehicles. In the paper a path following algorithm based on the non-orthogonal Serret–Frenet parametrization is presented. Such an approach is global and does not introduce any constraints to the robot description with respect to the path. It has been extensively studied recently. Hence, an experimental verification of the algorithm is proposed. The validation was conducted on a laboratory test–bed equipped with a redundant manipulator —the KINOVA ® Gen3 Ultra lightweight robot. In the paper a case study is proposed to compare simulation results and experimental measurements. It is an example how the mathematical legacy of the past centuries can be used for modern solutions. The experimental study confirms the practical suitability of the presented control algorithm.
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Paper Nr:	59
Title:	Dual-Arm Compliance Control with Robust Force Decomposition
Authors:	William Freidank, Konrad Ahlin and Stephen Balakirsky
Abstract:	Realtime, compliant control of dual-arm robots has been an open area of investigation for versatile object manipulation. Recent research has focused on leader-follower, hybrid, and impedance control techniques. This paper proposes a guaranteed-convergence artificial potential field in order to leverage its advantages in computational speed and functional quality. Additionally, compliance control is integrated using a novel force decomposition method. Experiments are performed on a 14 Degree-of-Freedom (DoF) dual-carriage rail, with two UR5e robots to validate the new method’s accuracy and demonstrate the feasibility of the unified controller.
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Paper Nr:	62
Title:	Robust Single Object Tracking and Following by Fusion Strategy
Authors:	Alejandro Olivas, Miguel Á. Muñoz-Bañón, Edison Velasco and Fernando Torres
Abstract:	Single Object Tracking methods are yet not robust enough because they may lose the target due to occlusions or changes in the target’s appearance, and it is difficult to detect automatically when they fail. To deal with these problems, we design a novel method to improve object tracking by fusing complementary types of trackers, taking advantage of each other’s strengths, with an Extended Kalman Filter to combine them in a probabilistic way. The environment perception is performed with a 3D LiDAR sensor, so we can track the object in the point cloud and also in the front-view image constructed from the point cloud. We use our tracker-fusion method in a mobile robot to follow pedestrians, also considering the dynamic obstacles in the environment to avoid them. We show that our method allows the robot to follow the target accurately during long experimental sessions where the trackers independently fail, demonstrating the robustness of our tracker-fusion strategy.
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Paper Nr:	63
Title:	Recognition and Position Estimation of Pears in Complex Orchards Using Stereo Camera and Deep Learning Algorithm
Authors:	Siyu Pan, Ayanori Yorozu, Akihisa Ohya and Tofeal Ahamed
Abstract:	Complex orchards present difficulties for fruit-picking robots due to shadows, overlapping fruits, and obstructing branches, resulting in errors during grasping. To improve the robustness of fruit-picking robots in the complex environment, this study compared the performance of different types of deep learning algorithms (Mask R-CNN, Faster R-CNN, and YOLACT) for pear recognition under different conditions (high and low light). Additionally, the ZED2 stereo camera with the algorithm of the highest precision for estimating the position of separating and aggregating pears. For pear recognition, the mAPs of Mask R-CNN were 95.22% and 99.45%, Faster R-CNN were 87.90% and 87.52%, YOLACT were 87.07% and 97.89% in the validation and test set. For position estimation, the mean error of separating pears was 0.017m, the standard deviation was 0.015m and the goodness of fit reached 0.896; The mean error of aggregating pears were 0.018m and the standard deviation was 0.021m and the goodness of fit reached 0.832. A pear recognition and positioning system was developed by ZED2 stereo camera with deep learning algorithm. It aimed to generate precise bounding boxes and recognize pears in a complex orchard within the range of 0.1 to 0.5m. The mean error of separating pears and less than 0.27m for aggregating pears. This demonstrated the system’s capability to accurately position and differentiate between individual pears and clusters in challenging orchard environments.
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Paper Nr:	70
Title:	Sensorless Reduction of Cane Oscillations Aimed at Improving Robotic Grapevine Winter Pruning
Authors:	Andrea Fimiani, Pierluigi Arpenti, Matteo Gatti and Fabio Ruggiero
Abstract:	Agricultural sector faces challenges like high labour costs and a shortage of qualified workers for repetitive tasks, leading to increased interest in agricultural robotics. Pruning is a focus for automation efforts worldwide. However, pruning robots struggle with slow and inaccurate vision systems, resulting in slow, costly, and potentially harmful operations for plants. This study aims to provide a reproducible and reliable method for detecting contact with grapevines during pruning, minimising potential damage, and improving vision system speed by reducing cane oscillations. The proposed approach uses a momentum-based observer, eliminating the need for force sensors. Experiments on Vitis vinifera cv. Pinot Noir canes validated this methodology.
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Paper Nr:	78
Title:	Simultaneous Planning of the Path and Supports of a Walking Robot
Authors:	Paula Mollá-Santamaría, Adrián Peidró, Arturo Gil, Óscar Reinoso and Luis Payá
Abstract:	In this paper we study the simultaneous planning of the path and leg supports of an eight-legged robot on uneven terrain. We use the A-star algorithm (A), which searches for the shortest path between two points. First, the terrain is modelled with a triangular mesh and the triangles are subdivided to take the centroids of these triangles as the search space of the A. Secondly, with respect to the original A*, the stability of the robot at each centroid is considered, so that the cost at a centroid is penalised if the robot is unstable (i.e., the robot slips and/or tips over), or the cost is zero if it is stable. The stability at each contact point is determined by calculating that the ground reaction at that point is contained in a linear approximation of the friction cone. Finally, the path, the contact points of each leg, as well as the robot’s posture at each position are obtained.
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Paper Nr:	99
Title:	Muscle-Like Soft Actuation for Motor-Less Robotic Exoskeletons
Authors:	Julian D. Colorado, John E. Bermeo, Fredy A. Cuellar, Catalina Alvarado-Rojas, Diego Mendez, Angela M. Iragorri and Ivan F. Mondragon
Abstract:	Shape Memory Alloys (SMAs) have opened new alternatives upon conventional actuation technologies used in robotics. SMA-based actuators are also known as muscle-like actuation mechanisms, in which Nickel titanium (Nitinol) fibers operate as artificial tendons for soft actuation. This paper explores the use and limits of tendon-like SMA actuation for a robotic exoskeleton to actively support hand motion rehabilitation.
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Paper Nr:	103
Title:	Development of Cart with Providing Constant Steerability Regardless of Loading Weight or Position: 3 rd Report on Evaluation of a Steering Assist System on Translational Movement
Authors:	Shunya Aoki, Sho Yokota, Akihiro Matsumoto, Daisuke Chugo, Satoshi Muramatsu, Katsuhiko Inagaki and Hiroshi Hashimoto
Abstract:	The steering of the shopping cart is affected by its load, leading to the need for the user to make corrective adjustments and apply excessive force. In this study, the system assists the steering of a shopping cart by actively steering casters based on the user’s operational intention estimated from the user’s force. This paper provides a brief introduction to the operational interface and the active steering caster. Subsequently, it elaborates on the steering assist system designed for translational movement. Furthermore, we conduct experiments to evaluate the steerability through subjective and objective assessments. These results confirmed that the system can support the operating force and corrective steering. In addition, subjects feel less weight than the conventional carts and have more intuitive sense than the conventional cart.
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Paper Nr:	119
Title:	Soft Robotic Tongue Mimicking English Pronunciation Movements 2 Report: Fabrication and Experimental Evaluation
Authors:	Evan Krisdityawan, Sho Yokota, Akihiro Matsumoto, Daisuke Chugo, Satoshi Muramatsu and Hiroshi Hashimoto
Abstract:	A novel soft robotic tongue mimicking the movements of English pronunciation was proposed, aiming at the learning support for English pronunciation. A soft robotic tongue’s system design and actuator arrangements have been proposed, and the Finite Element Methods (FEM) simulation for each deformation has been conducted. In this paper, we discussed two milestones: fabrication and experimental evaluation. The fabrication, molding, and casting method was applied to the model, and it was manufactured five times bigger than the original size of a human tongue. A silicone rubber Ecoflex 00-30 was utilized and poured into the mold that was preliminary printed with a 3D printer. Moreover, an experiment was conducted to confirm and evaluate the deformation patterns of English pronunciation movements. A ruler was used to measure the parameters in each deformation, such as bend and flap angle, and bulge height. It presented that bend and bulge deformations between the fabricated soft robotic tongue and simulated FEM results were likely the same; however, the flap deformation slightly differed in the experimental evaluation.
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Paper Nr:	131
Title:	A Decision-Making Architecture for Human-Robot Collaboration: Model Transferability
Authors:	Mehdi Sobhani, Jim Smith, Anthony Pipe and Angelika Peer
Abstract:	In this paper, we aim to demonstrate the potential for wider-ranging capabilities and ease of transferability of our recently developed decision-making architecture for human-robot collaboration. To this end, a somewhat related but different application-specific example from the generic one used in its development is chosen, a toy car assembling task in which a participant works together with a robot to perform the assembly task. In a “Wizard of Oz” fashion, a comparison is made between the participant’s reactions to working with the robot being controlled either by our architecture or by a human “Wizard” who is hidden from view. With regard to the generalisability of the architecture, we also wish to investigate whether specific models trained on the observed human behaviour in a generic assembly task also transfer to this more complex task. Therefore, pre-trained interaction models from a prior generic pick-and-place task are used again in this new application without any re-training. The architecture was implemented on a robotic arm. Participants worked with the robotic arm to perform the task of picking toy car parts one by one and assembling the car while collaborating with the robot. Each participant repeated the task 3 times for each condition, Model or Wizard, in a random order. At the end of each trial participants completed a PeRDITA questionnaire. First, a test to rule out significant differences was performed, which yielded no significant results for any of the subjective and objective measures. As not having a significant difference does not necessarily mean similarity of conditions, to check for similarity, a Bayesian comparison of the conditions was performed next, which indicated a high probability of similarity between the model and Wizard performance. The high similarity to human-like performance observed for this more complex task supports the claim for the transferability of the models trained on a more generic task.
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Paper Nr:	149
Title:	Development of Walking Assistance Orthosis by Inducing Trunk Rotation Using Leg Movement: 1 st Report on Prototype and Feasibility Experiment
Authors:	Harutaka Ooki, Sho Yokota, Akihiro Matumoto, Daisuke Chugo, Satoshi Muramatsu and Hiroshi Hashimoto
Abstract:	Walking is a whole-body movement including an upper body (trunk) and a lower body (pelvis and lower limbs), which is called the Spinal Engine Theory. Furthermore, there is a finding that the stride length and walking speed increases with the amount of trunk rotation. Based on this insight, there have been existing study that promotes rotation of the upper body to assist in walking. However, motors are used to assist the rotation of the upper body, which requires maintenance of the power supply and complicates the system. Therefore, this research aims to develop an orthosis that promotes gait by increasing the amount of trunk rotation without any active actuators. In particular, this paper reports on the basic experiment to confirm that the prototype can apply assistive torque to the trunk when the leg is raised.
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Paper Nr:	175
Title:	A Study on Gathering Staircase Information for Active Staircase Entry of Wheelchair Stair Climbing Assistive Devices
Authors:	Su-Hong Eom, Jeon-Min Kang, Ga-Young Kim and Eung-Hyuk Lee
Abstract:	Wheelchairs are the most commonly used auxiliary devices by people with mobility impairments, and autonomous driving technology has recently been applied to these wheelchairs using robot technology. However, in an autonomous driving environment, most stairs are recognized as obstacles. For autonomous driving on stairs, recognition of stairs information must be preceded. Currently, the classification of stairs into stairs and non-stairs is performed based on vision sensors and can be determined by a high recognition rate. However, the measurement and estimation of the riser height value, tread depth value, and angle of pitch value of stairs are not. Therefore, this study proposes a method of obtaining the shape information of stairs using 2D LiDAR. The proposed method measured the riser height and tread depth of stairs using the K-Means and RANSAC algorithm after obtaining the raw data by rotating the 2D LiDAR by 90 degrees, and based on this, the angle of pitch value was calculated. The riser height and tread depth values were determined by about ±13mm on average, and the angle of pitch value showed the accuracy of ±1° accuracy through applying a quantitative verification method for the proposed method.
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Paper Nr:	181
Title:	Longitudinal Motion Control of Underactuated Cruising AUVs for Acoustic Bottom Survey
Authors:	Kangsoo Kim
Abstract:	Longitudinal motion control approaches for underactuated cruising AUVs primarily tasked with acoustic bottom surveys are addressed. For controlling the longitudinal motion of a cruising AUV, we implemented waypoint-based depth control and terrain following approaches during simulated acoustic bottom survey missions. Simulation results revealed that the distinct motion control approaches significantly influence the pitch motion of the vehicle, thereby directly impacting the quality of the acoustic bottom survey results. The safety issue of a cruising AUV, particularly regarding the occurrence of bottom collisions during its near-bottom survey missions is also investigated in this research. Concerning the safety issue, we found that while traversing the same trackline, the likelihood of an AUV encountering a bottom collision varies considerably, based on the specific motion control approach being utilized.
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Paper Nr:	189
Title:	Sustainability in Robotic Process Automation: Proposing a Universal Implementation Model
Authors:	Christian Daase, Anuraag Pandey, Daniel Staegemann and Klaus Turowski
Abstract:	Robotic process automation (RPA) is a key technology for automating mundane, repetitive back-office tasks that are typically performed by human workers. Because RPA instantiations, known as software robots, operate partially with the same graphical user interfaces as humans and can only replicate the business processes for which they were previously designed, they can lack sustainability as they stop working when sudden changes occur. This paper argues that RPA endeavors should be planned as long-term journeys through the era of digital transformation. Based on a systematic literature review and interviews with experts from industries that have successfully implemented software robots, this study summarizes and proposes a universal model for sustainable RPA implementation. The model consists of three phases, from planning to development to maintenance and scaling of projects. Although thorough evaluation is required through careful application of the proposed workflows, a useful addition to the body of knowledge on RPA could be created as all design decisions were made with the approval of industry experts.
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Paper Nr:	7
Title:	Smooth Sliding Mode Control Based Technique of an Autonomous Underwater Vehicle Based Localization Using Obstacle Avoidance Strategy
Authors:	Fethi Demim, Abdenebi Rouigueb, Hadjira Belaidi, Ali Z. Messaoui, Khadir L. Bensseghieur, Ahmed Allam, Mohamed A. Benatia, Abdelmadjid Nouri and Abdelkrim Nemra
Abstract:	Navigating underwater environments presents serious challenges in control and localization technology. The successful navigation of uncharted territories requires autonomous maneuvers that achieve goals while avoiding obstacles, posing a significant problem to be addressed. Detection-based control using sensor data and obstacle avoidance technology are vital for the autonomy of Autonomous Underwater Vehicles (AUVs). This study focuses on developing a control method based on Sliding Mode Control (SMC) and utilizing an imaging sonar sensor for obstacle avoidance. The proposed approach includes a controller for pitch and depth control, enabling avoidance of stationary objects. A Gaussian potential function is employed to guide the AUV’s maneuvers and avoid obstructions. Numerous simulation results evaluate the control performance of the AUV in realistic simulation conditions, assessing accuracy and stability. The experimental in simulation results demonstrate the excellent performance of our approach in navigating various obstacles such as gentle rise, steep drop-off, and underwater walls, using seafloor environment simulation models.
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Paper Nr:	20
Title:	Design and Control of Wearable Ankle Robotic Device
Authors:	Ali Zakaria Messaoui, Mohamed A. Alouane, Mohamed Guiatni, Omar Mechali, Sbargoud Fazia, Zerdani Serine and Belimene C. Elmokhtar
Abstract:	The primary objective of this paper is to develop an ankle wearable robotic device, which involves two primary tasks: design and control. The design task focused on creating a comfortable, lightweight, and secure ankle exoskeleton robot; this task was achieved using SOLIDWORKS and considering all essential factors. For the control aspect of the exoskeleton, an Improved Optimized Homogeneous Twisting Control (IOHTC) approach was proposed to design a robust angular position control system. To ensure the stability of the control system, a homogeneous-Lyapunov function was used. Simulation results based on real gait data demonstrated consistency with the theoretical foundation, and a comparative analysis based on various performance indices confirmed the effectiveness and superiority of the proposed control law. Finally, several simulations have been conducted on the designed model using simscape multibody link to validate it.
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Paper Nr:	22
Title:	Real Time Orbital Object Recognition for Optical Space Surveillance Applications
Authors:	Radu Danescu, Attila Fuzes, Razvan Itu and Vlad Turcu
Abstract:	Artificial objects in various orbits surround the Earth, and many of these objects can be found within the low Earth orbit region (LEO). This orbital zone also contains a significant amount of space debris, which pose a tangible threat to space operations, necessitating close monitoring. Various sensors can be used for either tracking, knowing the target’s orbital parameters and observing it for updating them, or for surveillance, which can also discover new targets. Real time identification of the satellite as it is detected by the surveillance systems provides a mechanism for selecting the targets for stare and chase applications, to decide if a new satellite has been discovered, or to identify a satellite that has outdated orbital elements. This paper describes a system capable of real time surveillance and satellite identification using limited computing power. The system relies on detecting trajectory endpoints at discrete time intervals, and then using these endpoints for frame by frame trajectory prediction, which is then matched with detected tracklets. This way, the tracklets are identified in real time. The system has been tested by surveying real satellites, in real time, and the identification mechanism proved to work as expected.
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Paper Nr:	40
Title:	On-Board Estimation of Vehicle Speed and the Need of Braking Using Convolutional Neural Networks
Authors:	Razvan Itu and Radu Danescu
Abstract:	Detecting the ego-vehicle state is a challenging problem in the context of autonomous vehicles. Perception-based methods leverage information from on-board cameras and sensors to determine the surrounding traffic scene and vehicle state. Monocular based approaches are becoming more popular for driver assistance, and accurate vehicle speed prediction plays an important role for improving road safety. This research paper presents an implementation of a Convolutional Neural Network (CNN) model for vehicle velocity prediction using sequential image input, as well as an extended model that also features sensorial data as input. The CNN model is trained on a dataset featuring sets of 20 sequential images, captured from a moving car in a road traffic scene. The aim of the model is to predict the current vehicle speed based on the information encoded in the previous 20 frames. The model architecture consists of convolutional layers followed by fully connected layers, having a linear output layer for the ego-vehicle velocity prediction. We evaluate our proposed models and compare them using existing published work that features Recurrent Neural Networks (RNNs). We also examine the prediction of the brake pedal pressure required while driving.
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Paper Nr:	84
Title:	A Linear Regression Based-Approach to Collective Gas Source Localization
Authors:	Ronnier F. Rohrich, Luis F. Messias, Jose Lima and Andre Schneider de Oliveira
Abstract:	This work addresses the problem of gas leaks and proposes a search strategy for identifying the source of a gas leak within a virtual simulation environment. The research focuses on designing and implementing simulation, control, and gas source search packages using swarm robotics. The simulation employs numerical integration strategies, while the robot swarm control is based on potential fields theory. The location of the gas source using a weighted linear regression strategy is used to estimate the gas concentration gradient, which plays a crucial role in the optimization strategy employed. The paper presents an overview of the key concepts employed and their relevance to different stages of the problem and highlights the main results achieved through the chosen strategies. A significant outcome of this work is the development of reusable software packages applicable to various research contexts in mobile robotics.
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Paper Nr:	86
Title:	Zeroth-Order Optimization Attacks on Deep Reinforcement Learning-Based Lane Changing Algorithms for Autonomous Vehicles
Authors:	Dayu Zhang, Nasser L. Azad, Sebastian Fischmeister and Stefan Marksteiner
Abstract:	As Autonomous Vehicles (AVs) become prevalent, their reinforcement learning-based decision-making algorithms, especially those governing highway lane changes, are potentially vulnerable to adversarial attacks. This study investigates the vulnerability of Deep Q-Network (DQN) and Deep Deterministic Policy Gradient (DDPG) reinforcement learning algorithms to black-box attacks. We utilize zeroth-order optimization methods like ZO-SignSGD, allowing effective attacks without gradient information, revealing vulnerabilities in the existing systems. Our results demonstrate that these attacks can significantly degrade the performance of the AV, reducing their rewards by 60 percent and more. We also explore adversarial training as a defensive measure, which enhances the robustness of the DRL algorithms but at the expense of overall performance. Our findings underline the necessity of developing robust and secure reinforcement learning algorithms for AVs, urging further research into comprehensive defense strategies. The work is the first to apply zeroth-order optimization attacks on reinforcement learning in AVs, highlighting the imperative for balancing robustness and accuracy in AV algorithms.
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Paper Nr:	95
Title:	An Efficient Resilient MPC Scheme via Constraint Tightening Against Cyberattacks: Application to Vehicle Cruise Control
Authors:	Milad Farsi, Shuhao Bian, Nasser L. Azad, Xiaobing Shi and Andrew Walenstein
Abstract:	We propose a novel framework for designing a resilient Model Predictive Control (MPC) targeting uncertain linear systems under cyber attack. Assuming a periodic attack scenario, we model the system under Denial of Service (DoS) attack, also with measurement noise, as an uncertain linear system with parametric and additive uncertainty. To detect anomalies, we employ a Kalman filter-based approach. Then, through our observations of the intensity of the launched attack, we determine a range of possible values for the system matrices, as well as establish bounds of the additive uncertainty for the equivalent uncertain system. Leveraging a recent constraint tightening robust MPC method, we present an optimization-based resilient algorithm. Accordingly, we compute the uncertainty bounds and corresponding constraints offline for various attack magnitudes. Then, this data can be used efficiently in the MPC computations online. We demonstrate the effectiveness of the developed framework on the Adaptive Cruise Control (ACC) problem.
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Paper Nr:	108
Title:	Modelling of a 6DoF Robot with Integration of a Controller Structure for Investigating Trajectories and Kinematic Parameters
Authors:	Armin Schleinitz, Chris Schöberlein, Andre Sewohl, Holger Schlegel and Martin Dix
Abstract:	Knowledge of robot joint position as a function of TCP-position and pose is of outstanding importance, since position and pose are specified by the process. However, there is no generally applicable method for the inverse transformation. In addition to a kinematic analysis and the inverse transformation of a 6DoF robot, this work also presents the development of a multi-body model based on it. All components are linked in a drive-specific controller structure. To validate the overall model, the simulation-based drive torques are compared with the values of a real robot. Likewise, target and actual Tool Center Point (TCP) positions of a given trajectory are examined in the simulation model and compared with a real system. It was shown that in the simulation model, the realized trajectory exhibits only very slight deviations compared to the previous trajectory, but greater deviations compared to the real system. The overall model forms the basis for further analyses regarding kinematic joint parameters as a function of a given trajectory.
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Paper Nr:	120
Title:	Low-Cost Synchronization Techniques for KUKA Robots and External Axes in Low-Dynamic Processes
Authors:	Patrick Kaufmann, Holger Weber and Michael Vistein
Abstract:	Many industries, including electronics, automotive, aviation, and food, are increasingly using industrial robots to automate processes and improve quality, efficiency, and cost-effectiveness. High-volume industries like electronics and automotive can automate complex tasks very cost-efficient, while industries with lower volumes, such as aviation, require flexible and reliable automation solutions to remain competitive while keeping a closer eye on the costs. One important task is the synchronization of robot movements with an external axis. While there are very accurate synchronization options available, these can be very complex and costly. In particular in research or process development where requirements are changing frequently, more flexible and also low-cost solutions are required. This paper analyzes several cost-effective alternatives for the synchronization of a KUKA robot with an external axis.
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Paper Nr:	141
Title:	Evaluation of Low-Cost 3D Scanner Hardware for Clothing Industry
Authors:	Michael Danner, Elena A. Brake, Christian Decker, Matthias Rätsch, Yordan Kyosev and Katerina Rose
Abstract:	In recent years, the demand for accurate and efficient 3D body scanning technologies has increased, driven by the growing interest in personalised textile development and health care. This position paper presents the implementation of a novel 3D body scanner that integrates multiple RGB cameras and image stitching techniques to generate detailed point clouds and 3D mesh models. Our system significantly enhances the scanning process, achieving higher resolution and fidelity while reducing the cost, time and effort required for data acquisition and processing. Furthermore, we evaluate the potential use cases and applications of our 3D body scanner, focusing on the textile technology and health sectors. In textile development, the 3D scanner contributes to bespoke clothing production, allowing designers to construct made-to-measure garments, thus minimising waste and enhancing customer satisfaction through fitting clothing. In mental health care, the 3D body scanner can be employed as a tool for body image analysis, providing valuable insights into the psychological and emotional aspects of self-perception. By exploring the synergy between the 3D body scanner and these fields, we aim to foster interdisciplinary collaborations that drive advancements in personalisation, sustainability, and well-being.
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Paper Nr:	145
Title:	Shape Transformation with CycleGAN Using an Automobile as an Example
Authors:	Akira Nakajima and Hiroyuki Kobayashi
Abstract:	AI technology has developed remarkably in recent years, and AI-based image generation tools have spread rapidly. CycleGAN is one of the image generation AIs and specializes in image style transformation, and has the problem of being able to change colors and patterns but not shapes. The reason may be that the model considers the background as a part of the conversion target, which can be solved by removing the background. In this study, the number of backgrounds is limited to a certain number, and CycleGAN is used for shape transformation.The evaluation is done by comparing the result of this experiment with the image transformation when the input is an image with the background removed.Comparison of the proposed and conventional methods showed comparable results.
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Paper Nr:	186
Title:	LQR Combined with Fuzzy Control for 2-DOF Planar Robot Trajectories
Authors:	A. Hernandez-Pineda, I. Bezerra-Viana, M. Marques-Simoes and F. Carvalho Bezerra
Abstract:	Some tasks of robotic manipulators are performed using control techniques for trajectory tracking. These techniques ensure that the existing steady-state error between the desired and executed trajectories are close to zero. This work proposes a hybrid control scheme that enhances a traditional control approach with computational tuning optimization. The Linear Quadratic Regulator (LQR) controller is implemented by manipulating the state variables of the plant to be controlled. The optimization of this controller is related to the weighting variables of the cost function. Computational tuning using fuzzy logic is applied to adjust the weighting variables of LQR. The results demonstrate that the hybrid control optimal performance outperformed the traditional LQR controller in the trajectory following task for the two-degree-of-freedom planar robotic manipulator.
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Area 4 - Signal Processing, Sensors, Systems Modelling and Control

Full Papers

Paper Nr:	18
Title:	Non-Parallel Training Approach for Emotional Voice Conversion Using CycleGAN
Authors:	Mohamed Elsayed, Sama Hadhoud, Alaa Elsetohy, Menna Osman and Walid Gomaa
Abstract:	The focus of this research is proposing a nonparallel emotional voice conversion for Egyptian Arabic speech. This method aims to change emotion-related features of a speech signal without changing its lexical content or speaker identity. We relied on the assumption that any speech signal can be divided into content and style code and the conversion between different emotion domains is done by combining the target style code with the content code of the input speech signal. We evaluated the model using an Egyptian Arabic dataset covering two emotion domains and the conversion results were successful depending on a survey conducted on random people. Our purpose is to produce a state-of-the-art pre-trained model as it will be an unprecedented model in the Egyptian Arabic language as far as we are concerned.
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Paper Nr:	49
Title:	Towards a Novel Nonlinear PID Controller Tuned with Particle Swarm Optimization with Improved Performance for First Order Plus Time Delay (FOPTD) Systems
Authors:	Stefanos Charkoutsis and Mohamed Kara-Mohamed
Abstract:	The Proportional, Integral, and Derivative (PID) controller is ubiquitous in industry, facing nonlinear systems that it can struggle to compensate. The main limitation of PID is the trade-off between set-point tracking and disturbance rejection that causes control design issues affecting industrial outputs. This paper proposes a novel Nonlinear gains Proportional, Integral, and Derivative (NLPID) controller that shows improved results in the simultaneous set-point tracking and disturbance rejection, using time-varying gains, to control nonlinear systems. The paper also shows the performance of the proposed controller for the case of a First Order Plus Time Delay (FOPTD) system, which heavily exists in industry. The proposed NLPID controller is tuned using the Particle Swarm Optimization (PSO) algorithm. The proposed NLPID controller is simulated in MATLAB/Simulink and compared against PSO tuned PID controller (PSO PID), Internal Model Control based PID (IMC PID), and a PID controller with a nonlinear integral function gain (Son NLPID), for the FOPTD system. This study shows that the proposed NLPID provides a faster response, with minimized overshoot, maintaining excellent disturbance rejection without compromising stability or speed. The study also shows that the proposed NLPID controller is robust against parametric uncertainty.
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Paper Nr:	72
Title:	Stochastic Estimation of Fundamental and Harmonic Signal Components
Authors:	Chukwuemeka Aduba
Abstract:	The paper investigates the estimation of fundamental and harmonic components in power system signal using stochastic estimator concept. The power system signal is approximated with a stochastic linear system model where the phase and amplitude components are estimated using a Kalman filter (KF) and an Ensemble Kalman filter (EnKF). The power system signal is modeled in both continuous and discrete form and then represented in state-space approach. Simulation results show that EnKF estimates converge to KF estimates as the ensemble size increases while reducing the computational complexity for highly-dimensional stochastic systems.
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Paper Nr:	76
Title:	An Observer Design Method Using Ultra-Local Model for Autonomous Vehicles
Authors:	Daniel Fenyes, Tamas Hegedus, Vu Van Tan and Peter Gaspar
Abstract:	The paper presents a novel observer design algorithm for autonomous vehicles. The technique is based on the combination of a classical linear observer and the ultra-local model. The linear observer is easy to design and it requires only a linear model of the considered system. However, it performs poorly when the linear system cannot cover the system’s dynamics due to nonlinearities or unmodelled dynamics. The ultra-local model aims to compensate for the nonlinear effects and improve the overall performances of the observer. The proposed method is applied to a vehicle-oriented estimation problem: lateral velocity. The operation and the effectiveness of the presented algorithm is demonstrated through several test scenarios in CarSim and also using real-test measurements.
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Paper Nr:	130
Title:	Fixed-Time Tracking Control for a Class of Nonlinear Systems via Command Filtered Backstepping
Authors:	Wen-Nian Qi and Rui-Qi Dong
Abstract:	The fixed-time tracking control problem is addressed for a class of nonlinear systems. A novel command filtered backstepping control law including virtual control signals, fixed-time command filters, and error compensation signals is constructed. By the introduced fixed-time command filters, the problem of “explosion of complexity” caused by backstepping approach is avoided. Simultaneously, the filtering errors produced by the introduced fixed-time command filters are eliminated by the designed error compensation signals. It is proven that the resulted closed-loop tracking control system under the proposed command filtered backstepping control law is fixed-time stable.
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Short Papers

Paper Nr:	5
Title:	Secured Communication of Speech Signal Using the Discrete Cosine Transform Based on Hyperchaos-System
Authors:	Noureddine Aissaoui, Fethi Demim, Abdenebi Rouigueb, Hadjira Belaidi, Ali Z. Messaoui, Kahina Louadj, Abdelkrim Nemra, Ahmed Allam, Yasmine Saidi, Said Sadoudi and Mohamed S. Azzaz
Abstract:	This paper proposes a novel approach that combines chaos-based encryption and Discrete Cosine Transform (DCT) to ensure high-level speech security and robustness against attacks. In this approach, the encryption process is based on Lorenz’s hyperchaotic system, which utilizes the One Time Pad approach to encrypt the speech DCT coefficients. The effectiveness of this approach has been validated through experiments on two PCs interconnected via real-time serial communication links (USB-RS232), which showed that the original speech is effectively hidden, and the proposed solution is highly resistant to possible attacks. Moreover, the proposed solution can be implemented in real-time applications using technologies such as FPGA.
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Paper Nr:	25
Title:	Closing the Sim-to-Real Gap with Physics-Enhanced Neural ODEs
Authors:	Tobias Kamp, Johannes Ultsch and Jonathan Brembeck
Abstract:	A central task in engineering is the modelling of dynamical systems. In addition to first-principle methods, data-driven approaches leverage recent developments in machine learning to infer models from observations. Hybrid models aim to inherit the advantages of both, white- and black-box modelling approaches by combining the two methods in various ways. In this sense, Neural Ordinary Differential Equations (NODEs) proved to be a promising approach that deploys state-of-the-art ODE solvers and offers great modelling flexibility. In this work, an exemplary NODE setup is used to train low-dimensional artificial neural networks with physically meaningful outputs to enhance a dynamical model. The approach maintains the physical integrity of the model and offers the possibility to enforce physical laws during the training. Further, this work outlines how a confidence interval for the learned functions can be inferred based on the deployed training data. The robustness of the approach against noisy data and model uncertainties is investigated and a way to optimize model parameters alongside the neural networks is shown. Finally, the training routine is optimized with mini-batching and sub-sampling, which reduces the training duration in the given example by over 80 %.
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Paper Nr:	35
Title:	Effects Study of Sensors’ Placement on the Accuracy of a 3D TDOA-Based Localization System
Authors:	Ahcene Bellabas, Ammar Mesloub, Belaid Ghezali, Abdelmadjid Maali and Tahar Ziani
Abstract:	Time Difference of Arrival (TDOA) based measurements are used for passive localization systems in various applications. While significant research has been performed on the development of TDOA measurement-based approaches, there has been relatively little focus on the sensor deployment geometry which significantly impacts the location estimation accuracy. Therefore, a study on the effects of four sensors’ placement on location accuracy has been conducted. Several factors are considered in numerical simulations analysis which have an obvious effect on the localization accuracy. Based on the analysis of the Geometric Dilution of Precision (GDOP) performance metric, a comparison is conducted between square and star geometries. The results show that the star geometry gives better performance in terms of location estimation accuracy, especially when the main receiver is positioned within the polygon formed with baseline angles of 120°. Furthermore, the star geometry is used to study also the influence of sensor height and baseline length to achieve an optimum three-dimensional sensor placement with four sensors. The results can be applied to enhance the sensor deployment in 3D sensor geometry for TDOA-based localization systems.
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Paper Nr:	45
Title:	Multiphysics Simulation for the Optimization of an Optoelectronic-Based Tactile Sensor
Authors:	Gianluca Laudante, Olga Pennacchio and Salvatore Pirozzi
Abstract:	Robotic systems are more and more present in various contexts such as industrial, domestic, logistic, healthcare, and others. For this reason, robots are being used for increasingly complex tasks which require skills like dexterity and precision. These capabilities are achieved by means of sensory systems that give that robot the perception of the environment. Sensors, before being produced and distributed, need to be suitably designed in order to fulfil the specifics that a task requires. During the design process, simulation methods are really important to analyze the characteristics of a designed product before actually producing it, so as to avoid waste of time and money. This paper aims at proposing a method for simulating a tactile sensor based on optoelectronic technology considering both the optical and mechanical interfaces, as well as their coupling. Also, it exploits both simulation and experiment results in order to discuss the best choice for the shape to use for the realization of reflective cells.
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Paper Nr:	52
Title:	Ultra-Wideband Direct RF Sampling Transceiver Design
Authors:	Ahcene Bellabas, Ammar Mesloub, Belaid Ghezali, Abdelmadjid Maali and Tahar Ziani
Abstract:	This paper focuses on the design and development of a direct RF sampling transceiver for ultra-wideband (UWB) radar applications. By directly synthesizing and capturing the desired signal, direct RF sampling simplifies the system and reduces analog components. It overcomes the limitations of heterodyne transceiver architecture, particularly the presence of harmonics and spurious signals at the mixer output. This approach enables digital processing and offers flexibility for system reconfiguration. Advanced techniques and concepts in radio transceiver design methodology are explored, discussing the constraints involved in meeting system design requirements for optimal radar system performance. A design of a direct RF sampling transceiver architecture for given requirements set is proposed, which includes concise frequency planning, digital receiver design, and a direct RF waveform synthesis scheme. Furthermore, experimental results demonstrate the suitability of the proposed direct RF sampling transceiver for UWB radar applications.
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Paper Nr:	57
Title:	MultiSpectrum Inspection of Overhead Power Lines
Authors:	Ronnier F. Rohrich and Andre Schneider de Oliveira
Abstract:	Electric power transmission employs an extensive network of transmission and distribution lines to connect energy production plants with consumers. This architecture limits the extent and frequency of inspections and implementation of preventive maintenance programs. Robotic systems, which allow movement over transmission cables, have been introduced to address the difficulties of inspections in distribution and transmission lines. This paper introduces a novel method of multispectrum robotic inspection for transmission lines, which can perform predictive inspection and maintenance, and discusses a new composite sensor that analyzes the integrity of overhead lines in acoustic, thermal, visual, and reference spectra. The system is particularly designed to be incorporated into cable-inspection robots and moves over cables to provide a direct point of view of the transmission line components. The proposed method was evaluated using a calibration scenario and actual overhead power lines.
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Paper Nr:	64
Title:	Learning-Based Energy Consumption Model of Machining Processes Using Gaussian Process Regression
Authors:	Alicia Soto Bono, Alan McGibney, Susan Rea and Kritchai Witheephanich
Abstract:	Currently, the global energy mix is largely dominated by the use of fossil fuels, with the industrial sector accounting for a significant portion of this demand. This results in a significant carbon footprint. As such, the manufacturing industry must become active participants in reducing their impact on the environment through the realization of sustainable manufacturing practices. This study analyzes the performance of a data-driven model enhanced with machine learning techniques in order to build a digital twin that can update its parameters in real-time in response to dynamic changes in the energy consumption of a machining process. This type of model is suitable for the application of a higher-level controller, such as a model predictive controller to optimize the efficiency of the process operation. This paper proposes a digital twin modelling approach based on Gaussian process regression, which updates model parameters with closed-loop data from the process in real-time to retrain the model (evolving). The updating of the model online enables the model to maintain accuracy over time despite changes in the system’s dynamics.
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Paper Nr:	77
Title:	Lateral Control for Automated Vehicles Based on Model Predictive Control and Error-Based Ultra-Local Model
Authors:	Tamas Hegedus, Daniel Fenyes, Vu Van Tan and Peter Gaspar
Abstract:	The paper proposes a combined control design framework using Model Predictive Control (MPC) and ultralocal model-based methods. The main idea behind the control algorithm is to exploit the advantage of both approaches. During the control input computation, a simplified model is used, which has a significant impact on the computational cost. Moreover, the simplified model does not contain hardly measurable or varying vehicle-specific parameters, which makes the whole control design process easier. The ultra-local model is used to deal with the unmodeled dynamics of the vehicle, by which the performance of the control system can be increased. The effectiveness of the proposed control structure is demonstrated through trajectory tracking problem of autonomous vehicles. The whole algorithm is implemented in a high-fidelity vehicle dynamics simulation software, whose results are compared to an accurate model-based MPC in terms of computational cost and tracking accuracy.
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Paper Nr:	87
Title:	Dynamic Model of the Weighing Process of an Industrial Combination Scale: Model Development and Simulative Analysis of the Product Impact Force
Authors:	Felix Profe, Lucas Kostetzer and Christoph Ament
Abstract:	This work shows a weighing product model that characterizes the processes of product impact during the weighing procedure of a combination scale. Unfortunately, the product impact force does not exist as a sensor quantity and is difficult to measure. Another complicating factor in developing a product model is the large variety of products and their fall behaviour. Even with identical product properties, falling comprises strong stochastic influence. With the help of a discrete element method simulation model it was possible to directly calculate the product impact force. More than 20 different products were tested. The simulation can reproduce the random fall behaviour. Based on these analyses, a real-time capable product model was derived. The model is able to generate impact curves based on portion weight, particle weight, impact time, drop height, and impact duration. Impact duration and time of impact of an individual particle are changing based on random variables. Due to simplifications, restitution coefficient and particle shape is not considered. With larger particles there are deviations in comparison of simulation and product model. Due to the low computational effort, the model could be used, for example, as a system input for a real-time capable model of a weighing station.
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Paper Nr:	101
Title:	Wireless Remote Control of Low-Cost Smart Devices for No-Coders
Authors:	Leopoldo Armesto, Sara Blanc, Antonio González and Antonio Sala
Abstract:	This paper describes the development of a block programming tool, named Facilino, to remotely control multiple agents consisting of low-cost smart devices based on ESP32 and Arduino, such an intelligent house and a robot, using Bluetooth and WiFi (HTTP) in the context of educational applications. Facilino is based on Blockly, a library that has been adapted to create Arduino code from custom blocks. The new tool is combined with App Inventor to develop Apps with no-coders. The paper describes the preliminary results using this tool within research and development and academic activities.
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Paper Nr:	124
Title:	Using the Built-in iPhone Body Tracking System for Neurological Tests: The Example of Assessing Arm Weakness in Stroke Patients: A Preliminary Evaluation of Accuracy and Performance
Authors:	Vittorio Lippi, Isabelle D. Walz, Tobias Heimbach, Simone Meier, Jochen Brich, Christian Haverkamp and Christoph Maurer
Abstract:	Timely treatment of stroke is critical to minimize brain damage. Therefore, efforts are being made to educate the public on detecting stroke symptoms, e.g., face, arms, and speech test (FAST). In this position paper, we propose to perform the arm weakness test using the integrated video tracking from an iPhone—some general tests to assess the tracking quality and discuss potential critical points. The test has been performed on 4 stroke patients. The result is compared with the report of the clinician. Although presenting some limitations, the system proved to be able to detect arm weakness as a symptom of stroke. We envisage that introducing a portable body tracking system in such clinical tests will provide advantages in terms of objectivity, repeatability, and the possibility to record and compare groups of patients.
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Paper Nr:	142
Title:	Breast Cancer Epidemic Model and Optimal Control
Authors:	Martina Brunetti, Paolo Di Giamberardino, Daniela Iacoviello and Marialourdes Ingrosso
Abstract:	The breast cancer represents one of the most frequent disease diagnosed worldwide; with the modern improvements in medicine and technology a fast detection of tumor could allow a total recovery. In this paper, it is proposed a compartmental epidemiological model in which the female population is partitioned depending on the condition with respect to the tumor diagnosis. The model is identified referring to the population of a region of Italy, using real data; increasing levels of control are introduced, from noninvasive prevention to combination of surgery and chemotherapy. In the framework of optimal control, aiming at reducing the number of severe cases and of women dead by tumor, a suitable combination of control effort is determined, considering constraints in the containment measures. Numerical results stress the importance of prevention that at the very beginning increases the number of discovered positive diagnosis, and, successively, significantly contains the fatal consequences of breast cancer on the population by reducing the late diagnosis.
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Paper Nr:	152
Title:	Comprehensive Φ-Bonacci Index for Walking Ability Assessment in Paroxysmal Positional Vertigo: Role of Rehabilitation
Authors:	Nicoló Colistra, Luca Pietrosanti, Mohamed El Arayshi, Sara Maurantonio, Beatrice Francavilla, Piergiorgio Giacomini and Cristiano M. Verrelli
Abstract:	Very recent research directions have been devoted to providing a theoretical foundation to the experimental evidence that human movements, such as walking, are able to induce time-harmonic motor patterns. The resulting findings have shown that such harmonic structures are characterized by the golden ratio occurring as the ratio of the durations of the walking gait sub-phases that compose generalized Fibonacci sequences. A new comprehensive gait index, named Φ-bonacci gait number, and a new related experimental conjecture – concerning the position of the foot relative to the tibia – have been concurrently proposed to capture the most reliable and objective (quantitive) outcome measures (and their distortions in pathological subjects) of recursivity, asymmetry, consistency, and self-similarity (harmonicity) of the gait cycle. This paper provides, for the first time, experimental results on healthy and pathological gaits – related to benign paroxysmal positional vertigo (BPPV) – that fully support the aforementioned theoretical derivations.
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Paper Nr:	157
Title:	A Low-Cost Printed Circuit Board Design for External Force Measuring in Robotic Applications
Authors:	H. Meneses, V. Jarquin, Y. Miranda, C. Cordero, N. Delgado, K. Vargas and F. Ruíz
Abstract:	This paper presents a low-cost printed circuit board designed to measure external forces in several robotics applications. Its operating principle is based on capturing electrical resistance change coming from strain gauges attached to deformable beams in elastic force-torque sensors. This system offers great flexibility because users can adjust up to 8 Wheatstone bridge circuit in different configurations depending on their needs, their parameters as offset and amplification gain can easily be configured and the assembly process is intended to be fast using a pick-and-place machine and a soldering oven.
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Paper Nr:	163
Title:	Masry: A Text-to-Speech System for the Egyptian Arabic
Authors:	Ahmed H. Azab, Ahmed B. Zaky, Tetsuji Ogawa and Walid Gomaa
Abstract:	This paper presents the improvement and evaluation of Masry, an end-to-end system planned to synthesize Egyptian Arabic speech. The proposed approach leverages the capable Tacotron speech synthesis models, counting Tacotron1 and Tacotron2, and integrated with progressed vocoders – Griffin-Lim for Tacotron1 and HiFi-GAN for Tacotron2. By synthesizing waveforms from mel-spectrograms, Masry offers a comprehensive solution for generating natural and expressive Egyptian Arabic speech. To train and validate our system, we construct a dataset including a male speaker describing standard composing pieces and news content in Egyptian Arabic. The sampling rate of recorded data is 44100 Hz, guaranteeing constancy and richness within the synthesized speech output. The execution of our framework was fastidiously assessed through different measurements, with a specific center on the Mean Opinion Score (MOS). The experimental results demonstrated the prevalence of Tacotron2 over Tacotron1, yielding a MOS of 4.48 compared to 3.64. This emphasizes the system’s capacity to capture and duplicate the nuances of Egyptian Arabic speech more effectively. Besides, The assessment extended to include fundamental measurements such as word and character error rates (WER and CER). These metrics give a quantitative appraisal of the precision and exactness of the synthesized speech.
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Paper Nr:	172
Title:	Real-Time Material Identification Using Light Spectroscopy and Support Vector Machine (SVM)
Authors:	Masoud Shaloo and Gábor Princz
Abstract:	Material identification is vital in diverse industries such as automotive and aerospace, and industrial applications including machining, robotics, and smart manufacturing. Aerospace and automotive sectors deal with machining, drilling, pressing, or grinding of multi-material parts, requiring manual process parameter adjustments based on each material due to various inherent material properties causing delays in setup time resulting in extended throughput times, decreasing production rates and increasing costs. In addition, manual adjustment may lead to a decrease in the quality of the final part. Thus, there is a need for an automated system that can detect the material type in real-time and employ that information to dynamically adjust the machining, drilling, pressing, or grinding parameters. This paper focuses on merging a low-cost light spectroscopy sensor in the wavelength range of 410 nm (UV) to 940nm (IR) and support vector machine (SVM) to facilitate material identification on automated production lines. Various materials including aluminum, acrylonitrile butadiene styrene (ABS), wood, polyvinyl chloride (PVC), plain carbon steel, polyamide (PA), polylactic (PLA), and galvanized plain carbon steel were examined. The findings revealed that, except for PLA and aluminum, all materials achieved very high accuracy, recall, precision, and F1-score of 100%. PLA showed 90% accuracy and recall, along with 100% precision and 94.7% F1-score. Similarly, aluminum attained 95% accuracy and recall, 100% precision, and a 97% F1-score.
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Paper Nr:	179
Title:	Adaptive Direct Compensation of External Disturbances for MIMO Linear Systems with State-Delay
Authors:	Bui Van Huan, Alexey A. Margun, Artem S. Kremlev and Dmitrii Dobriborsci
Abstract:	In the paper we propose a new method for compensation of external disturbances in MIMO linear systems with unmeasured and delayed state vector. A state observer is used to estimate the state vector, which used in another external disturbance observer. All these estimates are used in a control law to ensure asymptotic convergence of the system outputs to zero and boundedness of all the closed loop signals. Proposed method is based on the use of the internal model principle and the extended error adaptation algorithm. It is assumed that the disturbance is the output of an autonomous linear generator with unknown parameters. To focus on compensation of external disturbances, it is assumed that the system is stable and the delay is known constant. The performance of the obtained results is confirmed using computer simulation in MATLAB Simulink.
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Paper Nr:	4
Title:	Improving the License Plate Character Segmentation Using Naïve Bayesian Network
Authors:	Abdenebi Rouigueb, Fethi Demim, Hadjira Belaidi, Ali Z. Messaoui, Mohamed A. Benatia and Badis Djamaa
Abstract:	Character segmentation plays a pivotal role in automatic license plate recognition (ALPR) systems. Assuming that plate localization has been accurately performed in a preceding stage, this paper mainly introduces a character segmentation algorithm based on combining standard segmentation techniques with prior knowledge about the plate’s structure. We propose employing a set of relevant features on-demand to classify detected blocks into either character or noise and to refine the segmentation when necessary. We suggest using the na ı̈ve Bayesian network (NBN) classifier for efficient combination of selected features. Incrementally, one after one, high computational cost features are computed and involved only if the low-cost ones cannot decisively determine the class of a block. Experimental results on a sample of Algerian car license plates demonstrate the efficiency of the proposed algorithm. It is designed to be more generic and easily extendable to integrate other features into the process.
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Paper Nr:	33
Title:	Single-Experiment Reconstructibility of Boolean Control Networks Revisited
Authors:	Guisen Wu and Jun Pang
Abstract:	We first demonstrate that, BCNs’ single-experiment reconstructibility has three additional forms in addition to its current definition, and briefly introduce the verification algorithms we design for these new definitions. These definitions and algorithms bring the following improvements to BCNs’ control theory. First, the solution algorithms of single-experiment reconstruction are enriched to cope with more different scenarios. Second, the verification problem of single-experiment reconstructibility is simplified. Finally, the essential relationship and difference between reconstruction and observation (which focuses on determining the initial state for a BCN), is further clarified.
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Paper Nr:	74
Title:	Design of Modular and Distributable Automation Software for PLCs
Authors:	Oscar Miguel-Escrig, Isabel Roselló-González and Julio-Ariel Romero-Pérez
Abstract:	Design and maintainability of modular automation software are common concerns nowadays. Common practice in industry usually overlooks the design phase of software, jumping directly into the coding phase, which typically results in poorly readable and maintainable code. In this work, it is shown how modular and hierarchically structured design of discrete event control systems, which is supported by Grafcet models, can be subsequently implemented in several devices distributed across the fieldbus. Besides, the resulting software is more readable and maintainable due to its similarities with the proposed Grafcet model. An example is provided showing how a distributed application can be tested in a centralized fashion.
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Paper Nr:	93
Title:	Joint-Based Robotic Impedance Control Transformations: An Experimental Study
Authors:	Carlos Saldarriaga, José J. Patiño, Carlos G. Helguero and Imin Kao
Abstract:	We present an experimental study in this paper to illustrate the effect of the Cartesian damping matrix on modulating the dynamic response of a robotic manipulator in impedance control. We first derive the transformation of the matrices of impedance control between the Cartesian and joint spaces using differential mathematics. Through experiments conducted on a redundant Franka Panda robot, it is demonstrated that the coupling term between damping and stiffness in impedance control derived from theoretical analysis, when transforming between the Cartesian and joint spaces, is important in stabilizing the dynamic response of the joints. We apply a methodology to modulate the dynamic response of a robot performing impedance control that allows us to study and select diagonal and off-diagonal elements of the Cartesian damping matrix according to the damping ratios and natural frequencies of the system in the modal space. In addition, we explain and show that an arbitrary selection of damping is counter-productive for robots to perform tasks under impedance control, and may lead to instability and out-of-range torques at the joints of the robotic manipulator.
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Paper Nr:	96
Title:	Tuning the Dynamic Response of a Redundant Robotic System Using Its Dominant Natural Frequencies
Authors:	Carlos Saldarriaga, Marcelo Fajardo-Pruna, Carlos G. Helguero and Jonathan Leon-Torres
Abstract:	Robotic systems often encounter challenges in achieving desired dynamic responses, especially when they possess redundant degrees of freedom. This paper proposes a methodology to identify a redundant robotic system’s dominant natural frequencies and tune its dynamic response through appropriate damping. The system’s natural frequencies are accurately identified by analyzing displacement data and leveraging the power of fast Fourier transform tools. These frequencies serve as critical parameters for modifying the response behavior, enabling enhanced control and stability. To validate the effectiveness of the proposed methodology, simulations are conducted on a 7-degree-of-freedom redundant Panda robotic manipulator. The results demonstrate the methodology’s potential to optimize the dynamic performance of complex robotic systems, opening avenues for improved efficiency, safety, and overall system performance.
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Paper Nr:	140
Title:	Bayesian State Estimation Using Constrained Zonotopes
Authors:	Lenka Kuklišová Pavelková
Abstract:	This paper proposes an approximate Bayesian recursive algorithm for the state estimation of a linear discrete time stochastic state space model. The involved state and observation noises are assumed to be bounded and uniformly distributed. The support of a posterior probability density function (pdf) is approximated by a constrained zonotope of an adjustable complexity. The behaviour of the proposed algorithm is illustrated by simulations and compared with other methods.
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