Eduardo Mario Nebot

Emeritus Professor, FIEEE, FTSE
University of Sydney

BIO: Eduardo Mario Nebot received his Bachelor's degree in Electrical Engineering from the Universidad Nacional del Sur, Argentina, and his M.S. and Ph.D. degrees from Colorado State University, USA. He is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) and the Fellow of the Australian Academy Technological Sciences and Engineering (FTSE).
He is a Professor at the University of Sydney in the School of Aerospace, Mechanical and Mechatronic Engineering. He was appointed the Patrick Chair of Automatic Control and Logistics in 2004 and served as the Director of the Australian Centre for Field Robotics from 2011 to 2020. He is now an Emeritus Professor at the Intelligent Transport Group at ACFR.
Professor Nebot has a substantial track record in robotics and automation. He has published more than 300 peer-reviewed conference and journal publications, and has given a large number of keynotes and industrial presentations. The major impact of his fundamental research is evident in autonomous systems, navigation, mining safety, and Intelligent Transport Systems.
Over the past 20 years, Professor Nebot has managed a large number of industrial collaboration research projects in the fields of Robotics and Automation. His fundamental research contributions have significantly impacted the profession, influencing the development of key autonomous technologies now deployed across various industrial environments, including mining, stevedoring, cargo handling, and urban road vehicles. His research group plays an active role in deploying innovative technology in the intelligent transport sector, focusing on smart vehicles.

Speech Title: Safe Deployment and integration of Autonomous Systems: Overcoming Challenges in Urban and Industrial Environments
Abstract: Over the past two decades, significant advancements in sensing, navigation, control, planning, and machine learning have driven the successful development and deployment of autonomous systems in diverse industries such as mining, stevedoring, and agriculture. By carefully defining and constraining the Operation Design Domain (ODD, the specific conditions under which these systems operate, researchers and practitioners have achieved an acceptable safety level. This keynote will delve into the fundamental problems that were addressed to facilitate the safe integration of robotic automation in industrial environments. The deployment of autonomous vehicles in urban environments presents formidable challenges, stemming primarily from the broad and intricate nature of the Operation Design Domain (ODD). These environments necessitate navigating complex interactions with diverse road users—ranging from pedestrians and cyclists to erratic drivers and adapting to varied traffic conditions. The presentation will discuss current projects in the intelligent transport system (ITS) area at ACFR and provide an indepth overview of the critical research challenges that must be addressed to enable safe autonomous applications in complex urban scenarios. By offering a comprehensive understanding of the challenges faced in both industrial and urban environments, this keynote aims to inspire continued innovation in the field of autonomous systems, paving the way for the safe and efficient integration of such technologies in increasingly complex environments.

Prof. Graziano Chesi

The University of Hong Kong

BIO: Graziano Chesi is a full 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. 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 founded the Technical Committee on Systems with Uncertainty of the IEEE Control Systems Society. He also served as chair of the Best Student Paper Award Committees of the IEEE Conference on Decision and Control and the IEEE Multi-Conference on Systems and Control. He authored the books "Homogeneous Polynomial Forms for Robustness Analysis of Uncertain Systems" and "Domain of Attraction: Analysis and Control via SOS Programming". He is a Fellow of the IEEE, AAIA and AIIA.

Speech Title: Analyzing Stability in 2D Systems via LMIs: From Pioneering to Recent Contributions

Abstract: 2D systems, also known as doubly-indexed systems, have gained an increasingly special attention in the control community, as they allow for modelling systems with more complex dynamics than the classical so called 1D systems where the signals are indexed by one variable only usually representing the time. Like for 1D systems, stability conditions have been proposed for 2D systems in the form of a linear matrix inequality (LMI) feasibility test, as such conditions may be tested by solving a convex optimization problem, and as such conditions may open the door for a number of developments such as establishing robust stability and designing stabilizing controllers. This talk aims at presenting, under a unified framework, various LMI stability conditions for 2D systems that have been proposed in the literature, from pioneering to recent contributions, in order to provide the reader with a comprehensive collection that may serve as a source of historical information as well as a platform for comparing the major characteristics of each condition.

 

Prof. Yongping Pan

Sun Yat-sen University, Shenzhen, China

BIO: Yongping Pan is a Professor who leads the Intelligent Robotics Lab at the Sun Yat-sen University, Shenzhen, China. He hold a Ph.D. degree in control theory and control engineering from the South China University of Technology, Guangzhou, China, and has over ten years of research experience in top universities in Singapore and Japan. His research interests lie in automatic control and machine learning for robotics. He has authored or co-authored more than 180 peer-reviewed academic papers, where over 130 papers were published in reputable refereed journals. His publications have attracted over 7200 and 5500 citations in the Google Scholar and Web of Science Core Collection, respectively. Dr. Pan is currently serving as the Chair of the IEEE Robotics and Automation Society Guangzhou Chapter and an Associate Editor of several top-tier journals published by IEEE and IFAC. He has served as an Organizing Committee Member of five international conferences and the Lead Workshop Organizer of the IEEE Conference on Decision and Control. He received the Best Automation Paper Finalist at the International Conference on Robotics and Automation, the Best Conference Paper Award at the International Conference on Control and Robotics, and the Best Conference Paper Finalist at the IEEE International Conference on Advanced Robotics and Mechatronics. He has been recognized as a Global Highly Cited Researcher by Clarivate, a Most Cited Chinese Researcher by Elsevier, and a World Top 2% Scientist by Stanford University. Furthermore, he has been invited to deliver academic talks at leading universities and conferences over 60 times worldwide.

Speech Title: Composite Learning Tracking and Interaction Control for Compliant Robots

Abstract: Due to the rapid population aging globally, the current trend of robotic research has been shifting from traditional industrial robots that are separated from humans to human-centered robots that coexist, cooperate, or collaborate with humans. A major motivation for introducing compliance to human-centered robots is physical human-robot interaction. This talk introduces our major results in composite learning tracking and interaction control for robots driven by compliant actuators. First, we develop a data-driven online learning methodology termed composite learning inspired by the cerebellum learning and control mechanism, and establish its rigorous theoretical results on consistent learning and strong robustness, which revolutionizes existing adaptive systems that are vulnerable and difficult to learn online. Then, we solve a series of key theoretical challenges in robotic applications of composite learning, and apply it to trajectory tracking, visual servoing, and interaction control of compliant robots, which improves their overall accuracy, safety, and naturalness.