Kostas J. Kyriakopoulos
Prof. Kostas Kyriakopoulos (http://users.ntua.gr/kkyria) received a Diploma in Mechanical Eng. (Honors) from NTUA (1985) and the MS (1987) & Ph.D (1991) in Electrical, Computer & Systems Eng. (ECSE) from Rensselaer Polytechnic Institute (RPI), Troy, NY. Between 1988-91 he did research at the NASA Center for Intelligent Robotic Systems for Space Exploration. Between 1991-93 he was an Assist. Prof. at ECSE - RPI and the NY State Center for Advanced Technology in Automation & Robotics. Since 1994 he has been with the Control Systems Laboratory (CSL) of the Mechanical Eng. Dept. at NTUA, where he served as Professor & Director of: (i) the Mechanical Design & Control Systems Division (ii) CSL (http://www.controlsystemslab.gr/index/), (iii) the Departmental Computation Lab while currently serves as Director of the Master’s Program on Automation Systems. His current interests are in the area of Embedded Control Systems applications in multi-Robot Autonomous Systems (Mobile, Underwater & Aerial Vehicles / Manipulators). He was awarded the G.Samaras award of academic excellence (NTUA), the Bodosakis Foundation Fellowship (1986-1989), the Alexander Onassis Foundation Fellowship (1989-1990) and an Alexander Von Humboldt Fellowship (1993). He has published ~350 papers to journals and fully refereed international conferences; he is Specialty Chief Editor for "Frontiers in Robotics and AI", senior editor of “IEEE/ASME Transactions on Mechatronics” and he serves in the editorial committees of a number of publications, while he has served as an administrative member of a number of international conferences. He has contributed to 40 projects funded by the EC and Greek Sources. He is an IEEE Fellow.
Decentralized Cooperation of Marine, Aerial and Ground Autonomous Systems
An important prerequisite for Persistent Autonomy of Multiple Robotic Systems is advanced, platform – level, motion planning & control to efficiently handle most of the platform-level motion issues in such a way as to allow motion be perceived from higher levels (Learning, Task Planning etc.) as a simple modality. Our efforts are centered around developing provable sensor-based motion planning and interaction control methodologies for autonomous systems. Our ultimate goal is to design sound interfaces of our provable control theoretic-based techniques with higher-level machine-intelligence based decision making schemes.
In the Uninhabited Underwater Vehicle (UUV) free motion case (e.g. inspection/surveillance), handling complex task missions and be robust enough to parameter uncertainties and disturbances due to real sea conditions is of significant. UUV specific needs such as limited energy and computational resources dictating low complexity motion control, model-free position and image based visual servoing schemes are presented. We proceed with a Vision-based Nonlinear Model Predictive Control scheme using a self-triggering mechanism to provide the next control update requiring a significantly smaller number of measurements from vision and less frequent computations of the control law, thus reducing processing time and energy consumption.
Underwater missions often require a level of interaction (e.g. valve/lever manipulation, tool grasping/carrying etc.) that can be accomplished by Underwater Vehicle Manipulation Systems (UVMS). We develop motion control algorithms for certain performance criteria such as optimal UVMS pose configuration to efficiently interact with the environment.
Finally, we present the problem of cooperative object transportation by multiple UVMSs in a constrained workspace with static obstacles, where the coordination relies solely on implicit communication arising from the physical interaction of the robots with the commonly grasped object. We propose a novel distributed leader-follower architecture, where the leading UVMS, which has knowledge of the object’s desired trajectory, tries to achieve the desired tracking behavior via an impedance control law, navigating in this way, the overall formation towards the goal configuration while avoiding collisions with the obstacles. On the other hand, the following UVMSs estimate the object’s desired trajectory via a novel prescribed performance estimation law and implement a similar impedance control law. The feedback relies on each UVMS’s force/torque measurements and no explicit data is exchanged online among the robots. Moreover, the control scheme adopts load sharing among the UVMSs according to their specific payload capabilities.
In the second part of our talk, a brief overview of our parallel research activities will be presented in areas such as:
- Multi-agent Systems: "Distributed multi-agent provable cooperation in both continuous & discrete domains",
- Aerial Robotics: “Dynamic Flights in Dynamic Environments”,
- Neuro-Robotics: “From Brain Machine Interfaces to Human Robot Interaction Applications”
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