Lecturer Profile |
Toshio Fukuda graduated from Waseda University, Tokyo, Japan in 1971 and received the Master of Engineering degree and the Doctor of Engineering degree both from the University of Tokyo, in 1973 and 1977, respectively, while he studied at Graduate School of Yale University in 1973-1975. He joined the National Mechanical Engineering Laboratory in Japan in 1977, while he was a research Scientist at University of Stuttgart in 1979-1981, the Science University of Tokyo in 1981, and then joined Department of Mechanical Engineering, Nagoya University, Japan in 1989. He is Professor Emeritus of Nagoya University. He has been working as Professor of Meijo University, Beijing Institute of Technology, Shenyang University of Technology, Institute of Automation, Chinese Academy of Science, Russell Springer Chaired Professor at UC Berkeley, Seoul National University, Advisory Professor of Industrial Technological Research Institute and etc. He is mainly engaging in the research fields of intelligent robotic system, micro and nano robotics, bio-robotic system, and technical diagnosis and error recovery system. He was the President of IEEE Robotics and Automation Society (1998-1999), Director of the IEEE Division X, Systems and Control (2001-2002), the Founding President of IEEE Nanotechnology Council (2002-2005), Region 10 Director (2013-2014), Director, IEEE Division X, Systems and Control (2017-2018) and is IEEE President-elect (2019). He was Editor-in-Chief of IEEE/ASME Trans. Mechatronics (2000-2002). He was the Founding General Chairman of IEEE International Conference on Intelligent Robots and Systems (IROS) held in Tokyo (1988). He was Founding Chair of the IEEE Workshop on Advanced Robotics Technology and Social Impacts (ARSO, 2005), Founding Chair of the IEEE Workshop on System Integration International (SII, 2008), Founding Chair of the International Symposium on Micro-Nano Mechatronics and Human Science (MHS, 1990-2012), Cyborg and Bionic Systems(2017), Intelligence and Safety of Robots (2018). He has received many awards such as IEEE Eugene Mittelmann Achievement Award (1997), IEEE Third Millennium Medal (2000) , IEEE Robotics and Automation Pioneer Award (2004), IEEE Transaction Automation Science and Engineering Googol Best New Application Paper Award (2007), George Saridis Leadership Award in Robotics and Automation (2009), IEEE Robotics and Automation Technical Field Award (2010). He received the IROS Harashima Award for Innovative Technologies (2011), Friendship Award of Liaoning Province PR China (2012), Friendship Award of Chinese Government (2014), IROS Distinguished Service Award (2015), Medal of Honor with Purple Ribbon from Japanese Government (2015). IEEE Fellow (1995). SICE Fellow (1995). JSME Fellow (2002), RSJ Fellow (2004), VRSJ Fellow (2011) and member of Science Council of Japan (2008-2013 ), and Academy of Engineering of Japan (2013-), Foreign Member of Chinese Academy of Sciences (2017). |
Lecture Abstract |
The monkey type of robot has many different locomotion modes, such as brachiation mode, swinging from branch to branch, biped mode, walking like humanoid, 4 leg mode, walking like animals, ladder mode, climbing up and descending ladders and others. This multi-locomotion robot (MLR) is studied how to make the locomotion stable and how to use and select the various locomotion modes. There are two methods for controlling the stable locomotion: The learning control method and the task oriented model based control. For the learning control methods, soft computing, reinforcement learning, neuro computing, and AI approach are applied for making the locomotion modes stable. For the task oriented model based control, Passive Dynamic Autonomous Control (PDAC) will be shown how the biped robot can make stable locomotion and then furthermore, applied for the quad locomotion mode and ladder climbing. Depending on the environment situations, the robot can select and adjust the best locomotion methods from biped mode to quad locomotion and vice versa. The robot stability can be augmented and improved by swinging the robot arms and using canes. Overall, the multi-locomotion robot (MLR) control methods show the more varieties of the stable locomotion for the robot. |
