Plenary Speaker 1

Title of the talk

Advanced Motion Control for Next-Generation Precision Mechatronics

Biography

Dr. Tom Oomen received the M.Sc. degree (cum laude) and Ph.D. degree from the Eindhoven University of Technology, Eindhoven, The Netherlands. He held visiting positions at KTH, Stockholm, Sweden, and at The University of Newcastle, Australia. Presently, he is an assistant professor with the Department of Mechanical Engineering at the Eindhoven University of Technology. He is a recipient of the Corus Young Talent Graduation Award and the 2015 IEEE Transactions on Control Systems Technology Outstanding Paper Award. He is Associate Editor on the IEEE Conference Editorial Board and IFAC Mechatronics. His research interests are in the field of system identification, robust control, and learning control, with applications in mechatronic systems.

Abstract

Manufacturing equipment and scientific instruments, including wafer scanners, printing systems, and microscopes, require precise and fast motions. Increasing requirements necessitate new lightweight system designs and enhanced control performance. These next-generation motion systems raise new challenges for advanced motion control. In particular, flexible mechanics need to be explicitly addressed, e.g., through overactuation, oversensing, inferential control, and position-dependent control. This in turn necessitates and justifies improved model quality compared to the state-of-the-art. New user-friendly, data-driven modeling techniques are developed, which directly deliver control-relevant models. In addition, numerically reliable algorithms are developed to deal with the high dimensionality and model complexity. Furthermore, the model is complemented with a robust-control-relevant uncertainty description, enabling high-performance robust control design. The feedback design is complemented with advanced feedforward control techniques, including learning control and machine-in-the-loop tuning of feedforward control parameters.

Plenary Speaker 2

Title of the talk

Team AIST-NEDO in DARPA Robotics Challenge Finals: Development, Lessons learned, and the Next

Biography

Shuuji Kajita Received M.E. (1985) and Dr.E. (1996) degrees in control engineering from Tokyo Institute of Technology, Japan. In 1985, he joined the Mechanical Engineering Laboratory, Ministry of International Trade and Industry. Meanwhile he was a Visiting Researcher at California Institute of Technology, 1996-1997. Currently he is a senior researcher at the National Institute of Advanced Industrial Science and Technology, Tsukuba, Japan, which was reorganized from AIST-MITI in April 2001. His research interests include robotics and control theory. He is a member of Society of Instrument and Control Engineers, Robotics Society of Japan and IEEE(Robotics and Automation Society). Recent book: Introduction to Humanoid Robotics, Springer, 2014 (co-authored with H.Hirukawa, K.Harada and K.Yokoi).

Abstract

The DARPA Robotics Challenge (DRC) was an international robotics competition for disaster response robots inspired from the accident of Fukushima Daiichi nuclear power plant after the Great East Japan Earthquake in 2011. At the DRC finals 2015, twenty three teams and their robots have competed with their skills on the field at Pomona, California, USA. To attend DRC finals, we organized Team AIST-NEDO by humanoid robotics researchers in AIST with financial support from New Energy and Industrial Technology Development Organization (NEDO). For the competition, we developed HRP-2Kai, a humanoid robot of 171cm high, 65kg weight, and 32 degrees of freedom. By using this robot, we developed a control system to perform car driving, door passing, valve turning, uneven terrain walking etc. Although the result of the competition was not very good (10th out of 23 teams), we learned many things from this challenge. For example, we experienced fall accidents and serious robot breakage during the DRC finals, therefore, we decided a protection system using airbags. Our preliminary experiments shows the airbags can effectively protect our robot even under the worst case of the system failure.