The 2014 edition of the workshop series will be held at Johannes Kepler University from May 22nd to May 23rd 2014. The organizers envision strengthening the cooperation between academia and industry. Therefore, the contributions and participation from industry is encouraged.
Prof. Jan Swevers (Katholieke Universiteit Leuven)
Jan Swevers received the M.Sc. degree in electrical engineering and the Ph.D. degree in mechanical engineering from the Katholieke Universiteit Leuven (KU Leuven), Belgium, in 1986 and 1992, respectively. He is full professor in the Department of Mechanical Engineering, Division Production Engineering, Machine Design and Automation (PMA), of K.U.Leuven. He is coordinating the thematic research team "MECO: Motion Estimation, Control and Optimization.
His research focuses on motion control and optimization: robust and iterative learning control design methodologies for (non-)linear multi-variable systems, identification and control of robot manipulators, modeling and compensation of friction in mechatronic systems, dynamic and embedded optimization for motion control systems. He is one of the promoters of OPTEC - the K.U.Leuven Center of Excellence: Optimization in Engineering, and member of the International Scientific Advisory Board of LINK-SIC - Linköping Center for Sensor Informatics and Control, Sweden.
Personal webpage: http://www.mech.kuleuven.be/meco
Optimal path following for robot systems
This talk presents an overview of our research at the Mechanical Engineering Department (KU Leuven, Belgium) on optimal path following for robots. Optimal path following for robots considers the problem of moving along a predetermined Cartesian geometric path, while some objective is minimized: e.g. motion time or energy loss.
This presentation first focusses on time-optimal path following. For simplified robot dynamics and convex constraints, the time-optimal path following problem transforms into a convex optimal control problem that can be solved efficiently up to a global optimum. A recursive log-barrier based solution method is derived for on-line path following that solves this convex problem in real-time by generating approximate solutions and hence yielding a trade-off between time-optimal behaviour and smoothness of actuator torques or energy efficiency.
Next two extension of this convex problem are discussed. A first extension considers constraints such as velocity-dependent torque constraints or torque rate constraints that destroy the convexity. An efficient sequential convex programming (SCP) approach is presented to solve the corresponding non-convex optimal control problems by writing the non-convex constraints as a difference of convex (DC) functions, resulting in convex–concave constraints. A second extension is the tube following problem. In practice it is often not required to follow a path exactly but only within a certain tolerance. By deviating from the path, within the allowable tolerance, one could gain in optimality. In our research the allowable deviation from the path is defined as a tube around the given geometric path, and the motion inside the tube is optimized. This transforms the path following problem to a tube following problem, which is not convex. However, a solution method is derived that can solve this non-convex problem efficiently. The presented approaches are illustrated by means of numerical simulations and experiments with a seven DOF robot.
This research has been performed in cooperation with Professor Moritz Diehl (now at the Institute of Microsystems Engineering (IMTEK) University of Freiburg, Germany) within the framework of OPTEC (KU Leuven’s Center-of-Excellence on Optimization in Engineering: http://www.kuleuven.be/optec)
Prof. Bruno Siciliano (Universita di Napoli Federico II)
Bruno Siciliano is Professor of Control and Robotics, and Director of the PRISMA Lab in the Department of Electrical Engineering and Information Technology at University of Naples Federico II. His research interests include force and visual control, human-robot interaction and service robotics. He has co-authored 7 books, 70 journal papers, 170 conference papers and book chapters. He has delivered 100 invited lectures and seminars at institutions worldwide, and he has been the recipient of several awards. He is a Fellow of IEEE, ASME and IFAC. He has served on the editorial boards of several peer-reviewed journals and has been chair of program and organizing committees of several international conferences. He is Co-Editor of the Springer Tracts in Advanced Robotics, and of the Springer Handbook of Robotics, which received the PROSE Award for Excellence in Physical Sciences & Mathematics and was also the winner in the category Engineering & Technology. His group has been granted thirteen European projects in the last five years. Professor Siciliano is the Past-President of the IEEE Robotics and Automation Society.
Personal webpage: http://wpage.unina.it/sicilian
The talk reports some recent results achieved within the framework of the European project DEXMART. An important issue in controlling a multi-fingered robotic hand grasping an object is the synthesis of the optimal contact points and the evaluation of the minimal contact forces able to guarantee the stability of the grasp and its feasibility. Both these problems can be solved online if suitable sensing information is available. In detail, using images taken by a camera mounted in an eye-in-hand configuration, a surface reconstruction algorithm and a grasp planner evolving in a synchronized parallel way have been designed for fast visual grasp of objects of unknown geometry. On the other hand, using finger tactile information and contact force measurements, an efficient algorithm was developed to compute the optimal contact forces, assuming that, during the execution of a manipulation task, both the position of the contact points on the object and the wrench to be balanced by the contact forces may change with time. Another goal pursued in DEXMART was the development of a human-like grasping approach inspired to neuroscience studies. In order to simplify the synthesis of a grasp, a configuration subspace based on few predominant postural synergies of the robotic hand has been computed. This approach was evaluated at kinematic level, showing that power and precise grasps can be performed using up to the third predominant synergy. The talk concludes by outlining active trends and perspectives in the field.
- Johannes Gerstmayr, LCM
- Michael Hofbaur, UMIT Hall i. Tirol
- Manfred Husty, University Innsbruck
- Wilfried Kubinger, FH Technikum Wien
- Ronald Naderer, FerRobotics Compliant Robot Technology
- Kurt Niel, FH Wels
- Justus Piater, University Innsbruck
- Andreas Pichler, Profactor
- Klemens Springer, University Linz
- Gerald Steinbauer, Graz University of Technology
- Markus Vincze, Vienna University of Technology
- Institute for Robotics
- Johannes Kepler University Linz
- Hubert Gattringer
- Klemens Springer
- Linz Center of Mechatronics GmbH (LCM)