Keynote Lectures

Abstract

In this presentation we give an overview of different control algorithms designed for both set point and trajectory tracking problems for a set of mobile robots moving in environment with static obstacles.  An arbitrary number of robots and obstacles can be used. 

Two new control algorithms are proposed. One is designed at the kinematic level and the second one at the dynamic level. It is assumed that we know all kinematical and dynamical parameters of robots. They can be different for all units which are used to build formation. Control scheme proposed at the kinematical level is based on a novel Vector Field Orientation (VFO) feedback control method developed in the Chair of Control and Systems Engineering with applications to a differentially-driven vehicle. We describe the control concept and control design methodology which originates from simple geometrical interpretations connected with the vehicle model structure. Novelty of the VFO method allows treating two considered control tasks – trajectory tracking and point stabilization – in a unified manner. Control system with VFO controllers reveal several practically desirable features like fast and non-oscillatory error convergence, simple interpretation of control inputs effect and, as a consequence, particular simplicity of the controller parametric synthesis. A new Lyapunov function candidate is proposed which takes into account control objectives and obstacle avoidance. It is shown that the proposed control algorithm is globally asymptotically stable to a small ball with radius which can be made arbitrary small.  Next this result is extended to the case when dynamics of robots is taken into account using standard back-stepping technique. Numerical stability and robustness to potential functions high values near robots and obstacles are very important issues which are widely discussed along with  computational complexity of all algorithms and their comparison with existing computational schemes. It is shown that increasing number of robots does not change computational burden significantly. Robots avoid obstacles both static and dynamic, in the case moving robots. It is shown how to avoid saddle points which are associated with obstacles in a set point control problem.

Theoretical considerations are supported by simulation results which are widely discussed. Next experimental work is presented and it clearly illustrates theoretical considerations. The multi-robot test-bed that was designed and implemented in the Chair of Control and Systems Engineering at Poznan University of Technology and uses vision-based localization. The camera is located above the task space and active LED markers are mounted on the top of differentially driven mobile robots. The image form the camera is transmitted to the vision server that identifies markers and stores their positions and orientations in the array. Then the resulting array is broadcasted to the robots with UDP packets. The data can be used to control the multi-robot system. Unsolved problems of control of multi agent systems are outlined at the end of presentation.

Abstract
Controlled or Active Magnetic Bearings (AMB) keep a rotor in a hovering position, without any contact, with no wear or lubrication. They are a typically mechatronic product, combining mechanical design, electronics, and information processing. The lecture gives a short survey on function and history, and then concentrates on industrial applications and actual research challenges. The main application area is turbo-machinery, with power ranges up to 20 MW and more. Recent examples are shown. The research topics include control aspects, reliability and safety issues, and demonstrate the ability of AMB as a component of smart machinery. A machine is being called smart if it makes best use of the internal informations about its state to improve its performance. The sensor data of the AMB are very useful as well for obtaining information about the machine process itself and they can be used for external data mining within the objectives of Industry 4.0 projects.

Abstract
There is a significant performance gap between human agents and robotic agents.  Closing this gap has been the goal or robotics research for many years now.  There are different opinions on how much progress the community as a whole has made in this regard.  In this talk, I would like to speculate on characteristics of solutions capable of closing that gap --- as opposed to the development of increasingly competent skills that serve specific applications but do not achieve the generality required to be part of closing the gap.  I will propose several characteristics and support their importance with experiments from the areas of grasping, interactive perception, and learning from interaction.  As these experiments can only be considered circumstantial evidence for my claims, I prefer to refer to these characteristics as “tricks”.  But my hope is, of course, that these tricks will develop into foundational components for the generation of versatile autonomous robot behavior.

Abstract
Rough terrain robotics is a new emerging research field that has stimulated a high number of works during recent years with the aim to provide more autonomy for outdoor vehicles. The talk will discuss the critical role of design and control in the development of autonomous off-road mobile robot. We will focus on articulated wheeled structure and particularly hybrid wheeled-legged locomotion, and show how they can be useful for autonomous outdoor applications, because articulated wheeled rovers offers both advantages of wheels and legs i.e. velocity for the first and obstacles crossing for the second. Their legs kinematics and compliance will be optimized for ensuring both reactive obstacle crossing and postural balance. High-level control will be also addressed by presenting recent advances in path planning and motion generation for mobile robots navigating autonomously over rough terrain. The main issue here is how we can obtain efficient prediction of rover stability and mobility for on-line path and motion planning.