报 告 人：Ming Luo
工作单位：Worcester Polytechnic Institute, USA
Dr. Ming Luo received the B.Eng. degree in Tianjin Polytechnic University Electronics and Information Engineering Department, Tianjin, China, in 2010, the M.Sc. degrees from Southeastern Louisiana University Integrated Science and Technology program, Hammond, LA, USA in 2012 and the PhD. degree in Robotic Engineering from Worcester Polytechnic Institute, Worcester, MA, USA in 2017. His research interests include soft robotics snake robots, and origami robots. He has publications on soft robotics, Bioinspiration & Biomimetics, IEEE Robotics and Automation Letters and other high rank conference papers like ICRA and IROS. His WPI soft robotics snake got the first place on the IEEE International Conference on Robotics and Automation (ICRA) soft robotics speed competition in 2017. He also received the IEEE International Conference on Robotics and Automation (ICRA) and WPI international travel funding in 2017. He reviewed many high impact factor journals, such as soft robotics, Bioinspiration and Biomimetics, IEEE Transactions on Control Systems Technology and Journal of Medical Robotics Research. Currently, he holds the CTO robotics development position on POWERHIVE LLC in USA.
Advantages of soft robotic systems over traditional robots include compliant adaptation to unstructured or unknown environments, organic bio-inspired mobility and manipulation, and increased safety for human robot. However, current soft robot platforms suffer from a lack of accurate theoretical dynamic models, proprioceptive measurements and efficient control and motion planning algorithm, which impede advancements toward full autonomy. This talk introduce the pressure-operated soft robotic snake platform “WPI SRS” which promises inherent flexibility and versatility to operate on complex and unpredictable environments compared to traditional snake robots made of rigid linkage chains. In addition, this thesis addresses the fundamental robotics challenges of the soft robot: Modeling, Sensing, Control and Motion planning and enables the technologies for autonomous soft robotics.