Prof. Dan Zhang
(Keynote Speaker)

Fellow of CAE, EIC, ASME, CSME

York University, Canada

Keynote Lecture: Structural Synthesis and Performance Study of Generalized Parallel Manipulators

Abstract: Although Conventional Parallel Manipulators (CPMs) have potential high rigidity, high accuracy, and high loading capacities, CPMs are not being widely adopted by industry due to limitations of their existing performance capabilities, including (1) the position and orientation workspaces of CPMs are relatively small due to the rigid end moving platform; (2) on account of the linear dependence of the axes of kinematic joints in the serial limbs,  the independent serial kinematic chains are hard to avoid singular configurations; (3) since the serial kinematic chains are independent, the overall stiffness of the manipulator is changed as the configuration of the CPM changes.

The overarching vision of this research is to overcome the limitations of CPMs – to develop new design methodologies and technologies for next-generation high performance Generalized Parallel Manipulators (GPMs) which include GPMs with configurable platforms for task adaptability, GPMs with coupling sub-chains to improve the global stiffness and GPMs with articulated moving platforms to enlarge the rotational angles. Thus, these new GPMs will effectively overcome the limitations of CPMs and be used for manufacturing applications including high precision assembly, fast product handling, milling and surface finishing as well as applications in other industries. Some research outcomes will be presented in this talk, and the design methodologies for structural synthesis of GPMs are developed, in addition, the theories for improving performance of GPMs are discussed. It will enable technology transfer of these techniques to industry applications by focusing on the critical technology gaps and rigorous experiments.

Biography: Dr. Dan Zhang is a Kaneff Professor and Tier 1 York Research Chair in Advanced Robotics and Mechatronics in the Department of Mechanical Engineering at York University. Dr. Zhang was a Canada Research Chair in Advanced Robotics and Automation, was a founding Chair of the Department of Automotive, Mechanical, and Manufacturing Engineering with the Faculty of Engineering & Applied Science at Ontario Tech University. He received his Ph.D. in Mechanical Engineering from Laval University, Canada, in June 2000.

Dr. Zhang's research interests include synthesis and optimization of parallel and hybrid mechanisms; generalized parallel mechanisms research; reconfigurable robots; innovation design of parallel robots: parallelization of serial robots; micro/nano manipulation and mems devices (e.g., sensors); rescue robots; smart biomedical instruments (e.g., exoskeleton robots and rehabilitation robotics); AI/robotics/autonomous systems; Aerial and Underwater Robotics; Artificial Intelligence for Robotics; intelligent reconfigurable adaptive landing gear and manipulator (manipulander).

Dr. Zhang’s contributions to and leadership within the field of robotic and automation have been recognized with several prestigious awards, within his own university (Kaneff Professorship, Tier 1 York Research Chair in Advanced Robotics and Mechatronics, Research Excellence Award both from university level and faculty level), the Province of Ontario (Early Researcher Award), the professional societies (Fellow of CAE, Fellow of the ASME, Fellow of the EIC and Fellow of the CSME), and federal funding agencies (Canada Research Chair in January 2009 and renewed in January 2014). Besides, he was awarded the Inaugural Teaching Excellence by the Faculty of Engineering and Applied Science of UOIT in 2006 and the Best Professor Award by UOIT Engineering Students' Society in 2012.

Dr. Zhang has published 217 journal papers and 182 conference papers, 12 books, 9 book chapters and numerous other technical publications. Dr. Zhang has served as a General Chair for 51 International Conferences and delivered 94 keynote speeches. Dr. Zhang is listed as the World’s Top Two Percent Researchers by Stanford’s Standardized Citation Indicators in 2020.

Prof. Xinjun Liu
(Keynote Speaker)

"Cheung Kong" Chair Professor, MO Chair of IFToMM China-Beijing

Tsinghua University, China

Keynote Lecture: New mode and robotic equipment innovation for large-scale structures in-situ machining

Abstract: The history of human civilization development is a history of cognitive development. Newton told us that there are laws in nature, and laws can be recognized. So, human beings’ curiosity and exploration of the natural world have made great creations and inventions. After investigating the constructing law and behavior rule of the natural life, a mobile machining robot is proposed for the in-situ machining of large-scale structures. Three key issues, i.e. type synthesis, performance evaluation and dimension optimization are introduced to design the robot. The cutting experiment on a large-scale space cabin structure with 3.5m in diameter shows that the machining precision can be 0.04mm, which is much higher than the manufacturing requirement. Based on this study, we confirm that roboticization, miniaturization and portability in the machining of large-scale structural parts are possible.

Biography: Xinjun Liu is a Full Professor with Tenure in Department of Mechanical Engineering at Tsinghua University, Beijing, China. He is the "Cheung Kong" Chair Professor, and the winner of National Outstanding Youth Fund of China. He is currently the MO Chair of International Federation for the Promotion of Mechanism and Machine Science (IFToMM) China-Beijing and the Director of the Beijing Key Laboratory of Precision and Ultra-precision Manufacturing Equipment and Control. From 2000 to 2001, he worked as a Postdoctoral Researcher at Tsinghua University. He was a Visiting Researcher at Seoul National University, Seoul, Korea in 2002-2003. He was the Alexander von Humboldt (AvH) Research Fellow at University of Stuttgart in Germany from 2004 to 2005. He was the Visiting Professor with Prof. Dr. Reimund Neugebauer at Fraunhofer Institute for Machine Tools and Forming Technology, Germany, in August of 2007. He has published over 190 papers in refereed journals and refereed conference proceedings and has been selected as Elsevier's Most Cited Chinese Researchers for seven consecutive years from 2014 to 2020, 90 authorized patents, and three books (including one book in English). His research interests include smart robotics, parallel mechanisms and robots, machining robots, and advanced and smart manufacturing equipment.

Prof. Qiang Liu
(Keynote Speaker)

Dean of Jiangxi Research Institute of Beihang University

Beihang University, China

Keynote Lecture: Study on advanced numerical control and intelligent machining technologies

Biography: LIU Qiang, PhD., Professor of School of Mechanical Engineering and Automation at Beihang University. He received his BSc degree from the Central-South University in 1983, MSc degree and Ph.D. from Beihang University in 1989 and 2000 respectively. He studied as a visiting scholar and joint PhD candidate in Prof. Y. Altintas’ Manufacturing Automation Laboratory (MAL), at The University of British Columbia during 1996~1998. He used to be the dean of School of Mechanical Engineering and Automation during 2000~2007, and the dean of Jiangxi Research Institute of Beihang University.

Currently his professional affiliations include: the Associate Chairman of National Technical Committee for Standardization of Machine Tool Numerical Control System (SAC/TC367), the Chief Specialist of Innovation Work Group for Intelligent NC Machine Tool at National Technical Committee for Standardization of Metal Cutting Machine Tools (SAC/TC22/WG2), the member of the National Intelligent Manufacturing Expert Committee, etc.

His research interests focus on the intelligent machining and advanced numerical control technologies. He received several awards, including Top Award on Science and Technology of China Mechanical Industry, Excellent Teacher, and United Technologies Corporation Rong Hong Endowment (U.S. A.), etc.

Prof. Makoto Iwasaki
(Keynote Speaker)

IEEE 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. Qiaokang Liang
(Invited Speaker)

Hunan University, China

Invited Lecture: FBG Wrist Force sensing system for Collaborative Robots

Abstract: Collaborative robots equipped with multicomponent force-sensing systems at the human–robot–environment interaction interface provides competitive advantages, such as collaboration, flexibility, and reliability. Most wrist force sensors use the cross-beam structures with electrical strain gauge measurement technologies. This article proposes an approach to develop a new ultrathin three-axis fiber Bragg grating (FBG) force sensor. First, an appropriate elastomer with embedded floating beams in ultrathin dimensions to measure three-axis force is designed using a finite-element method (FEM). A temperature-compensated extreme learning machine (TC-ELM) network is proposed to achieve the characteristics of low coupling and temperature compensation (TC). Calibration and experiment on the UR5 robot are conducted, and results illustrate that the highest sensitivity of the sensor can reach 34.07 pm/N, the repetition error is less than 5%, and the maximum coupling error is less than 0.21%. The maximum temperature drift after compensation is less than 20 pm. The proposed sensor has identical output as a commercial sensor and performs well in dynamic loading and unloading operations. Finally, comparison results from commercial and academic alternatives demonstrate the interest of the proposed approach to develop high-performance and integrated force-sensing solutions.

Biography: Qiaokang Liang (Member, IEEE) received the Ph.D. degree in control science and engineering from the University of Science and Technology of China, Hefei, China, in 2011. He is currently a Professor with the College of Electrical and Information Engineering, Hunan University, Changsha, China, where he is the Vice Director of the Hunan Key Laboratory of Intelligent Robot Technology in Electronic Manufacturing. He also serves as an Assistant Director of the National Engineering Research Center for Robot Vision Perception and Control. His research interests include robotics and mechatronics, biomimetic sensing, advanced robot technology, and human–computer interaction.

Dr. Tan Zhang
(Invited Speaker)

Shenzhen Technology University, China

Invited Lecture: Sim2Real Learning of Obstacle Avoidance for Robotic Manipulators in Uncertain Environments

Abstract: Obstacle avoidance for robotic manipulators can be challenging when they operate in unstructured environments. This problem is probed with the sim-to-real (sim2real) deep reinforcement learning, such that a moving policy of the robotic arm is learnt in a simulator and then adapted to the real world. However, the problem of sim2real adaptation is notoriously difficult. This work proposes a unified representation of obstacles and targets to capture the underlying dynamics of the environment and a flexible end-to-end model combining the unified representation with the deep reinforcement learning control module. Such a representation is agnostic to the shape and appearance of the underlying objects, which simplifies and unifies the scene representation in both simulated and real worlds. A vision-based actor-critic framework is implemented by devising a bounding box predictor module. The predictor estimates the 3D bounding boxes of obstacles and targets from the RGB-D input. The features extracted by the predictor are fed into the policy network, and all the modules are jointly trained. This makes the policy learn object-aware scene representation, which leads to a data-efficient learning of the obstacle avoidance policy.

Biography: Tan Zhang, assistant professor at Shenzhen Technology University. Her research interests lie in robotic learning and its applications in motion planning and robot manipulation. She was awarded as Shenzhen high-level overseas talents in 2018. She received her Ph.D. degree in Biomedical Engineering from the University of Saskatchewan in Canada in 2015 and then worked as a postdoctoral researcher in the Department of Mechanical Engineering at the University of Toronto, Canada. From 2018 to 2020, she worked as an associated researcher in visual computing research center at Shenzhen University. She has published a dozen of papers in prestigious journals and conferences in robotics, e.g., IEEE Robotics and Automation Letters (RA-L), IEEE Robotics and Automation Magazine (RAM), Mechanism and Machine Theory (MMT), Robotics: Science and Systems (RSS), IEEE/ASME Conference on Advanced Intelligent Mechatronics (AIM), etc.