Shugen Ma received his Ph.D. degrees in Mechanical Engineering Science from Tokyo Institute of Technology (Japan) in 1991. From 1991 to 1992 he was a Research Engineer with Komatsu Ltd and from 1992 to 1993 he was a Visiting Scholar at the University of California, Riverside (USA). He joined the Department of Systems Engineering, Ibaraki University, Japan, as an Assistant Professor in July 1993. In October 2005, he moved to Ritsumeikan University, where he is currently a professor in the Department of Robotics. He is also the director of Shanghai Robotics Institute at Shanghai University. He is/was a visiting professor at John Hopkins University, Shenyang Institute of Automation, Tianjin University, Shanghai University, Beijing University of Chemical Technology, Harbin Institute of Technology, and Xi’an Jiaotong University, respectively.
His current research interests include the design and control of environment-adaptive robots, field robotics, and Bio-robotics. He has published over 450 papers in refereed professional journal and international conference proceedings. He has also developed more than 30 novel robot systems, filed 60 patents, and supervised 40 doctoral students to graduation.
He is an IEEE fellow, a JSME fellow, and a member of the SICE and the Robotics Society of Japan. He is the general chair of IROS2022@Kyoto, was an associate Editor of the IEEE Transaction on Robotics from December 2003 to November 2007, an Editor of Advanced Robotics from April 2007, and serves many societies and conferences.
Speech Title: In-pipe Robots for Inspection of Pipelines
Abstract: Pipelines are widely used in industries and our daily lives as essential components of infrastructures. To prevent critical leakage accidents, regular inspection and rapid repair of pipelines are necessary. I introduce the in-pipe robots we have developed in this talk, especially a multilink-articulated robot with omnidirectional and hemispherical wheels. In the multilink-articulated robot, a pair of hemispherical wheels attach at the end of the robot to quickly align the steering direction. In contrast, the omnidirectional wheels generate enough propelling force forward and backward. I will also present an anisotropic shadow-based operation-assistant system, which is composed of only a single illuminator and a camera.
Yigung Hong is currently a professor and deputy director of Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University, Shanghai. He was a professor of Academy of Mathematics and Systems Science, Chinese Academy of Science, and served as the director of the Key Lab of Systems and Control, CAS, and the director of the Information Technology Division, National Center for Mathematics and Interdisciplinary Sciences, CAS. He is a Fellow of IEEE, a Fellow of Chinese Association for Artificial Intelligence, and a Fellow of Chinese Association of Automation (CAA). Moreover, he is the chair of Technical Committee of Control Theory (TCCT) of CAA. Additionally, he was a board of governor of IEEE Control Systems Society (CSS), the chair of IEEE CSS membership and public information committee and the chair of IEEE CSS chapter activities committee. His current research interests include nonlinear control, multi-agent systems, distributed optimization and game, machine learning, and social networks. He serves as Editor-in-Chief of Control Theory and Technology. He also serves or served as Associate Editors for many journals, including the IEEE Transactions on Automatic Control, IEEE Transactions on Control of Network Systems, and IEEE Control Systems Magazine. Moreover, he is a recipient of the Guang Zhaozhi Award at the Chinese Control Conference, Young Author Prize of the IFAC World Congress, Young Scientist Award of CAS, the Youth Award for Science and Technology of China, and the National Natural Science Prize of China.
Speech Title: Distributed optimization analysis and design of multi-agent systems
Abstract: In this talk, we start with some of our recent results on distributed optimization problems with constraints over a multi-agent network, and we propose different algorithms to solve them with convergence analysis. To be strict, we focus on two important problems: one is distributed resource allocation with many applications in smart grids, wireless communication, and traffic control. Then we introduce a framework to provide distributed optimization algorithms for physical systems.
Graziano Chesi is a Professor at the Department of Electrical and Electronic Engineering of the University of Hong Kong. He received the Laurea in Information Engineering from the University of Florence and the PhD in Systems Engineering from the University of Bologna. Before joining the University of Hong Kong, he was with the Department of Information Engineering of the University of Siena. He served as Associate Editor for various journals, including Automatica, the European Journal of Control, the IEEE Control Systems Letters, the IEEE Transactions on Automatic Control, the IEEE Transactions on Computational Biology and Bioinformatics, and Systems and Control Letters. He also served as chair of the Best Student Paper Award Committees for the IEEE Conference on Decision and Control and the IEEE Multi-Conference on Systems and Control. He is author of the books "Homogeneous Polynomial Forms for Robustness Analysis of Uncertain Systems" (Springer 2009) and "Domain of Attraction: Analysis and Control via SOS Programming" (Springer 2011). He was elevated to IEEE Fellow for contributions to control of nonlinear and multi-dimensional systems upon evaluation by the IEEE Control Systems Society.
Speech Title: LMI-Based Multiple-View Triangulation for Generalized Cameras
Abstract: Multiple-view triangulation is a fundamental problem in computer vision, which consists of estimating the position of a scene point by exploiting its image projections on several cameras. This talk considers the situation where the scene point is observed by generalized cameras, i.e., cameras that can be modeled by a spherical projection followed by a perspective one. Indeed, these cameras can have a much larger field of view than traditional pinhole cameras and, hence, they may be preferable in various applications. It is shown that convex optimization problems with linear matrix inequalities can be formulated to obtain the sought estimate, in particular, by introducing a criterion which consists of minimizing the angles between the projections on the sphere of the available image projections and the corresponding projections of the estimate.