|Keynote Speakers - MVAID 2022|
Prof. Qixin Cao, Shanghai Jiao Tong University, China
Title:The Key Technologies of Autonomous Movement and Grasping for Composite Robot
Abstract: Composite robot is a new kind of robot which integrates autonomous movement and manipulator technology. Compared with traditional robots, they have higher mobility and flexibility, and are recognized as indispensable production equipment for intelligent factories. Although, as early as in the 1980s, the concept prototype of composite robot was developed by the German factory. Due to the limitation of the complexity of autonomous movement and grasping technology, this kind of composite robot has been difficult to be promoted and applied. More recently, advances in machine vision and deep learning have made it possible to use composite robots in a variety of industries. Therefore, our laboratory has carried out in-depth research on key technologies of mobile grasping of composite robots, including environment perception and modeling, object recognition and grasping, task planning and implementation, and the construction of RSFC(Robot software Functional Component) platform. RSFC platform can be combined and configured according to different working environments and tasks of the composite robot, so that the composite robot can realize autonomous visual grasping and assembly functions. Experiments verify that the autonomous composite robot based on RSFC platform has high flexibility, and can flexibly realize model grasping and assembly tasks with/without model. In addition, the results of JDX Robot Challenge show that the robot has the characteristics of fast execution and high reliability.
Experience: Qixin Cao, a professor of Shanghai Jiao Tong University, doctoral supervisor, member of National Robot Standardization Technical Committee (SAC/TC591), standing committee member of Intelligent Robot Professional Committee of Chinese Association for Artificial Intelligence, advisory committee member of Robot Competition Working Committee of Chinese Association of Automation, standing committee member of Artificial Intelligence Branch of Chinese Society for Agricultural Machinery, member of the Standing Committee of The Chinese Research Hospital Society for Medical And Industrial Transformation and Health Industry Integration branch, visiting professor of Miyazaki University and visiting professor of The University of Electro-Communications. His research interests include machine vision, ubiquitous robotics, mobile robotics, medical robotics and agricultural robotics. He has published more than 120 EI&SCI papers and obtained more than 90 national invention patents. He has won one Second prize of National Science and Technology Progress Award, one First prize of Science and Technology Award of Chinese Society for Artificial Intelligence, five Provincial and Ministerial Science and Technology awards, and two Second prizes of National Teaching Achievements; Guided students to win the National University Students "Challenge Cup" Special prize; three First prizes for Provincial and Ministerial Teaching Achievements. In 2017 and 2019, I guided my students to win the gold medal and bronze award in the graduation Design Competition of "Star Cup" of China Machinery Industry Excellent Engineers Education Alliance.
Assoc. Prof. Pavel Loskot, IEEE Senior Member, Zhejiang University-University of Illinois at Urbana-Champaign Institute (ZJUI), China
Experience: Pavel Loskot joined the ZJU-UIUC Institute in January 2021 as the Associate Professor after being nearly 14 years with Swansea University in the UK. He received his PhD degree in Wireless Communications from the University of Alberta in Canada, and the MSc and BSc degrees in Radioelectronics and Biomedical Electronics, respectively, from the Czech Technical University of Prague in the Czech Republic. He is the Senior Member of the IEEE, Fellow of the Higher Education Academy in the UK, and the Recognized Research Supervisor of the UK Council for Graduate Education. His current research interest focuses on problems involving statistical signal processing and importing methods from Telecommunication Engineering and Computer Science to other disciplines in order to improve the efficiency and the information power of system modeling and analysis.