RobCE 2023 | Nanjing, China

RobCE 2023 Speakers

Prof. Valentina Emilia Balas
(Keynote Speaker)

Aurel Vlaicu University of Arad, Romania

Keynote Lecture: Optimal Traffic Control

Abstract: This presentation addresses some soft computing methods used in design and control of complex systems. Fuzzy logic methodologies and expert systems are good techniques to solve control problems for many practical applications. We introduce some applications of intelligent systems. In the meantime, we provide them with elements of deterministic knowledge and, we can assist the design with simulations.
A specific class of fuzzy controllers are introduced. This talk presents the evolution of a novel concept for the management of the traffic on highways, based on the constant time to collision criterion. The origin of this approach is the fuzzy assistance for drivers by Inverse Time to Collision. The next stages are the Constant Time to Collision Criterion and the Constant Time to Collision platoons. The advantages of this concept also called “the two second rule” are the perfect adaptation to the cars’ velocity and the ease with which drivers can apply it mentally. Some simulations are given to illustrate the effectiveness and applicability of the proposed criterion.

Biography: Valentina E. Balas is currently Full Professor in the Department of Automatics and Applied Software at the Faculty of Engineering, “Aurel Vlaicu” University of Arad, Romania. She holds a Ph.D. Cum Laude, in Applied Electronics and Telecommunications from Polytechnic University of Timisoara. Dr. Balas is author of more than 400 research papers in refereed journals and International Conferences. Her research interests are in Intelligent Systems, Fuzzy Control, Soft Computing, Smart Sensors, Information Fusion, Modeling and Simulation.
She is the Editor-in Chief to International Journal of Advanced Intelligence Paradigms (IJAIP) and to International Journal of Computational Systems Engineering (IJCSysE), member in Editorial Board member of several national and international journals and is evaluator expert for national, international projects and PhD Thesis.
Dr. Balas is the director of Intelligent Systems Research Centre in Aurel Vlaicu University of Arad and Director of the Department of International Relations, Programs and Projects in the same university.
She served as General Chair of the International Workshop Soft Computing and Applications (SOFA) in nine editions organized in the interval 2005-2020 and held in Romania and Hungary. Dr. Balas participated in many international conferences as Organizer, Honorary Chair, Session Chair, member in Steering, Advisory or International Program Committees and Keynote Speaker.
Recently she was working in a national project with EU funding support: BioCell-NanoART = Novel Bio-inspired Cellular Nano-Architectures - For Digital Integrated Circuits, 3M Euro from National Authority for Scientific Research and Innovation.
She is a member of European Society for Fuzzy Logic and Technology (EUSFLAT), member of Society for Industrial and Applied Mathematics (SIAM) and a Senior Member IEEE, member in Technical Committee – Fuzzy Systems (IEEE Computational Intelligence Society), chair of the Task Force 14 in Technical Committee – Emergent Technologies (IEEE CIS), member in Technical Committee – Soft Computing (IEEE SMCS).
Dr. Balas was past Vice-president (responsible with Awards) of IFSA - International Fuzzy Systems Association Council (2013-2015), is a Joint Secretary of the Governing Council of Forum for Interdisciplinary Mathematics (FIM), - A Multidisciplinary Academic Body, India and recipient of the "Tudor Tanasescu" Prize from the Romanian Academy for contributions in the field of soft computing methods (2019).

Prof. Hesheng Wang
(Keynote Speaker)

Shanghai Jiao Tong University, China

Keynote Lecture: Visual Servoing of Robots

Abstract: Visual servoing is an important technique that uses visual information for the feedback control of robots. By directly incorporating visual feedback in the dynamic control loop, it is possible to enhance the system stability and the control performance. Many challenges appear when robots come to our daily life. Compare to industrial applications, the robot need deal with many unexpected situations in unstructured environments. The system should estimate the depth information, the target information and many other information online. In this talk, various visual servoing approaches will be presented to work in unstructured environments. These methods are also implemented in many robot systems such as manipulator, mobile robot, soft robot, quadrotor and so on.

Biography: Hesheng Wang received the Ph.D. degree in Automation & Computer-Aided Engineering from the Chinese University of Hong Kong. Currently, he is a Professor of Department of Automation, Shanghai Jiao Tong University, China. He has published more than 200 papers in refereed journals and conferences. He is an associate editor of IEEE Transactions on Automation Science Engineering, IEEE Robotics and Automation Letters, Assembly Automation and International Journal of Humanoid Robotics, a Technical Editor of IEEE/ASME Transactions on Mechatronics. He served as an associate editor for IEEE Transactions on Robotics from 2015 to 2019. He was the general chair of IEEE RCAR2016 and IEEE ROBIO2022, and program chair of IEEE AIM2019 and IEEE ROBIO2014. He was a recipient of Shanghai Rising Star Award in 2014, The National Science Fund for Outstanding Young Scholars in 2017, Shanghai Shuguang Scholar in 2019 and The National Science Fund for Distinguished Young Scholars in 2022. He will be the General Chair of IEEE/RSJ IROS2025.

Prof.Jianwei Zhang
(Keynote Speaker)

Universität Hamburg, Germany

Keynote Lecture: Crossmodal Learning and control for Dexterous Robots Tasks

Abstract: Robot systems are needed to solve some real-world challenges by combining machine automation with realization of cognitive abilities in ICT systems. There has been substantial progress in deep neural networks and AI in terms of individual data-driven benchmarking, however, such existing data-driven systems are not yet crossmodal, they are not robust in a dynamic and changing world. My talk will first introduce concepts of cognitive systems which allows a robot to better understand multimodal scenarios by integration of knowledge and learning and then the necessary modules to enhance the robot intelligence level. Then I will explain how a robot can enhance its model as a result of learning from experiences; and how such cross-modal learning methods can be realized in intelligent robots. At the end, I will demonstrate several novel robot systems with dexterous walking and manipulation skills in potential service applications.

Biography:Jianwei Zhang is professor and Director of Technical Aspects of Multimodal Systems, Department of Informatics, Universität Hamburg, Germany. He is Academician of National Academy of Engineering Sciences in Germany and Academy of Sciences and Humanities in Hamburg Germany. He is also Distinguished Visiting Professor of Tsinghua University. He received both his Bachelor of Engineering (1986, Computer Control, with distinction) and Master of Engineering (1989, AI) at the Department of Computer Science of Tsinghua University, Beijing, China, and his PhD (1994, Robotics) at the Institute of Real-Time Computer Systems and Robotics, Department of Computer Science, University of Karlsruhe, Germany. Jianwei Zhang´s research interests include multimodal information processing, cognitive sensor fusion for robot perception, real-time learning algorithms, robot dynamics, modelling of sensory-motor control tasks, etc. In these areas, he has published over 500 journal and conference and received multiple best paper awards at several international conferences.

Prof. Makoto Iwasaki
(Keynote Speaker)
IEEE Fellow, IEE Japan Fellow

Nagoya Institute of Technology, Japan

Keynote Lecture: GA-Based Optimization in Mechatronic Systems: System Identification and Controller Design

Abstract: Fast-response and high-precision motion control is one of indispensable techniques in a wide variety of high performance mechatronic systems including micro and/or nano scale motion, such as data storage devices, machine tools, manufacturing tools for electronics components, and industrial robots, from the standpoints of high productivity, high quality of products, and total cost reduction. In those applications, the required specifications in the motion performance, e.g. response/settling time, trajectory/settling accuracy, etc., should be sufficiently achieved. In addition, the robustness against disturbances and/or uncertainties, the mechanical vibration suppression, and the adaptation capability against variations in mechanisms should be essential properties to be provided in the performance.

The keynote speech presents practical optimization techniques based on a genetic algorithm (GA) for mechatronic systems, especially focusing on auto-tuning approaches in system identification and motion controller design. Comparing to conventional manual tuning techniques, the auto-tuning technique can save the time and cost of controller tuning by skilled engineers, can reduce performance deviation among products, and can achieve higher control performance. The technique consists of two main processes: one is an autonomous system identification process, involving in the use of actual motion profiles of system. The other is, on the other hand, an autonomous control gain tuning process in the frequency and time domains, involving in the use of GA, which satisfies the required tuning control specifications, e.g., control performance, execution time, stability, and practical applicability in industries. The proposed technique has been practically evaluated through experiments performed, by giving examples in industrial applications to a galvano scanner in laser drilling manufacturing and an actual six-axis industrial robot.

Biography: Makoto Iwasaki received the B.S., M.S., and Dr. Eng. degrees in electrical and computer engineering from Nagoya Institute of Technology, Nagoya, Japan, in 1986, 1988, and 1991, respectively. He is currently a Professor at the Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology.
As professional contributions of the IEEE, he has participated in various organizing services, such as, a Co-Editors-in-Chief for IEEE Transactions on Industrial Electronics since 2016, a Vice President for Planning and Development in term of 2018 to 2021, etc. He is IEEE fellow class 2015 for "contributions to fast and precise positioning in motion controller design".
He has received a number of awards and honors for his academic contributions, like the Best Paper and Technical Awards of IEE Japan, the Nagamori Award, the Ichimura Prize, and the Commendation for Science and Technology by the Japanese Minister of Education, respectively. He is also a fellow of IEE Japan, and a member of Science Council of Japan.
His current research interests are the applications of control theories to linear/nonlinear modeling and precision positioning, through various collaborative research activities with industries.

Prof. Zhengtao Ding
(Keynote Speaker)

University of Manchester, UK

Keynote Lecture: Network-Connected Formation and Cooperative Control of Autonomous Systems

Abstract: In this network-connected world, many tasks require coordination and cooperation of subsystems/agents via network connection. Multi-agent systems are good examples of interplay between network communication and control applications. This talk will briefly review some fundamental concepts of multi-agent systems and important developments in consensus control, distributed optimization, and their applications in robotics and power and energy systems. It will then focus on formation and cooperative control mobile robots and autonomous vehicles. In particular, the talk will cover in details of some important methods, such as affine and bearing-only formation control algorithms which rely on the stress matrices and bearing. It will also cover distributed motion control algorithms to ensure autonomous overtaking of autonomous vehicles in a dynamic environment using the Artificial Potential Field (APF) method based on a robust autonomous vehicle platoon system.

Biography: Zhengtao Ding received B.Eng. degree from Tsinghua University, Beijing, China, and M.Sc. degree in systems and control, and the Ph.D. degree in control systems from the University of Manchester Institute of Science and Technology, Manchester, U.K. After working in Singapore for ten years, he joined the University of Manchester in 2003, where he is currently the Professor of Control Systems and the Head of Control, Robotics and Communication Division. He has authored/co-authored three books, including the book Nonlinear and Adaptive Control Systems (IET, 2013) and has published over 300 research articles. His research interests include nonlinear and adaptive control theory and their applications, more recently network-based control, distributed optimization and distributed learning, with applications to power systems and robotics. Prof. Ding serves/has served as the Editor in Chief of Drones and Autonomous Vehicles, Subject Chef Editor of Nonlinear Control for Frontiers, and Associate Editor for Scientific Reports, IEEE Transactions on Automatic Control, IEEE Transactions on Circuit and Systems II, IEEE Control Systems Letters, Transactions of the Institute of Measurement and Control, Control Theory and Technology, Unmanned Systems and several other journals. He is a member of IEEE Technical Committee on Nonlinear Systems and Control, IEEE Technical Committee on Intelligent Control, and IFAC Technical Committee on Adaptive and Learning Systems. He is a fellow of The Alan Turing Institute, the UK’s national institute for data science and artificial intelligence.