Yantao Shen, Professor

University of Nevada, Reno, USA

 

 

 

 

Title: Enabling High-Performance Biomimetic Deformable Robots by Rigid Elements Linked Shape-Morphing Mechanisms

Abstract: Soft robots are generally made of soft materials or soft mechanisms with a continuously deformable structure. However, there are many research issues and challenges remaining in current soft robotic systems, including complex modeling of soft structure and control difficulty due to highly nonlinear and uncertain dynamics, low actuation accuracy and power efficiency, and small load capability due to weak softness modulation. These problems limit the potential applications of soft robots and are still the hurdles in front of extending soft robotics technologies. In this talk, towards an effective solution/framework for dealing with these problems and challenges, we propose and present the novel semi-soft or semi-rigid mechanism, called Rigid Elements Linked Shape-Morphing mechanism (RELSM), for enabling high-performance soft robots. The mechanism adopts both active and passive rigid scissor elements/arrays with variable stiffness and low degrees of freedom (DOF) to form the actively soft morphing structure. The articulating morphing structure has the capability of accurately configuring the system shape and producing significant forces, which allows it to be compliant and robust enough to adapt to contacts and impacts but stiff enough to apply required forces. These advantages are validated through extensive simulations and experiments and indicates the RELSM method is a competitive technical solution to advance soft robotic research. Based on the RELSM mechanism, several bioinspired and highly deformable robotic systems and their actuation methodologies will be reviewed and their potential applications and extensions will also be discussed, including applications in emergency and industrial activities, medicine, space or planetary exploration, and new concept UAVs.

Biography: Yantao Shen received his BS and MS degrees from Beijing Institute of Technology, and the Ph. D. degree from the Chinese University of Hong Kong. He is currently an Associate Professor in the Department of Electrical and Biomedical Engineering at University of Nevada, Reno (UNR). Dr. Shen’s current research interests include Bio-robotics/-mechatronics, Bioinstrumentation and Automation, Sensors and Actuators, Visual Servoing, and Tactile & Haptic Interfaces. He has authored-coauthored two book chapters and over 100 peer-reviewed journal and conference papers, and co-holds four US patents. His research papers have been nominated/selected as a finalist for Best Vision Paper Award in the 2001 IEEE ICRA, a finalist for Best Conference Paper Award in the 2007 IEEE RO-MAN, a winner of the T. J. Tarn Best Robotics Paper Award in the 2009 IEEE ROBIO, a finalist for Best Conference Paper Award in the 2014 IEEE ROBIO, a finalist for Best Paper Award in Biomimetics in the 2015 IEEE ROBIO, and a finalist for Best Student Paper Award in the 2017 IROS. Dr. Shen’s research is currently supported by NSF and National Robotics Initiative (through NIH R01), as well as NASA and local agencies. He was a recipient of NSF CAREER Award, the 2015 Excellence Award from UNR College of Engineering and the UNR IEEE Outstanding Electrical Engineering Professor in both 2010 and 2011.


 

 

Lu Liu, Professor

Department of Mechanical and Biomedical Engineering,

City University of Hong Kong, HK SAR, China.

 

 

 

Title: Output Consensus of Networked Linear Multi-Agent Systems

Abstract: In this talk, the output consensus problem of heterogeneous linear multi-agent systems is introduced. With a low gain approach, a novel distributed dynamic output feedback control law is developed to solve the problem. It is noted that all agent systems have general and multi-input multi-output linear dynamics. The proposed control law requires neither communication of internal state nor additional stable dynamic compensator, which not only reduces the dimension of the controller but also greatly facilitates the implementation of the controller.

Biography: Dr. Lu Liu received her Ph.D. degree in 2008 in the Department of Mechanical and Automation Engineering, Chinese University of Hong Kong, Hong Kong. From 2009 to 2012, she was an Assistant Professor in The University of Tokyo, Japan, and then a Lecturer in The University of Nottingham, United Kingdom. After that, she joined the Department of Mechanical and Biomedical Engineering, City University of Hong Kong, Hong Kong, where she is currently an Associate Professor. Her research interests are primarily in networked dynamical systems, control theory and applications and biomedical devices. She received the Best Paper Award (Guan Zhaozhi Award) of the 27th Chinese Control Conference in 2008, the Shimemura Young Author Award of the 11th Asian Control Conference in 2017, the Zhang Si-Ying Outstanding Youth Paper Award of the 30th Chinese Control and Decision Conference in 2018. She also received the President’s Award and the Outstanding Supervisor Award in City University of Hong Kong in 2018 and 2017, respectively.

 


 

Cheng Jun, Professor

Shenzhen Institutes of Advanced Technology,

Chinese Academy of Sciences, China

 

 

 

Title: Human action capturing and recognition system

Abstract: Human action capture and recognition technology has been playing an important role in many areas, including human-computer interaction, virtual reality, animation manufacturing, athletic training, etc. This talk will give an introduction to some vision-based and accelerator-based human action capture technologies. Binocular system, in a manner similar to human vision, is easier to obtain the three-dimensional information. A local matching algorithm integrated with temporal motion cues between consecutive frames is proposed to effectively predict disparity maps in real-time. After 3D information being obtained, human action can be recognized.

Biography: Jun Cheng is a professor and founding director of Laboratory for Human Machine Control, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences. His research interests include Machine Vision, Human Computer Interaction and Robotics. He has published 160+ papers and obtained 60+ authorized patents. He acquired some awards including Wuwenjun AI Scientific and Technological Progress Award, Guangdong Scientific and Technological Progress Award, etc.


 

Ming Yang, Professor

Department of Automation,

Shanghai Jiao Tong University, China.

 

 

 

 

Title: History and Current Status of Driverless Car in China

Abstract: The global driverless car market is expected to be worth $7 trillion by 2050. Like their foreign competitors, Chinese companies have taken up ambitious goals in developing driverless vehicles, related sensors and controllers. In northwestern Shanghai, China’s first dedicated driverless car testing park opened in June 2016. Similar projects have been approved in cities like Beijing, Chongqing and Wuhan. Several successful demonstrations have been made in China during the past several years.

Biography: Ming Yang received the Ph.D. degree in computer sciences from Tsinghua University, Beijing, China, in 2003. After two-year post-doc experiences in INRIA, he joined Shanghai Jiao Tong University in 2005. He is currently a Full Professor with the Department of Automation. His main research interests include autonomous driving, assistant driving and cooperative driving, mobile robots, machine vision, and high definition maps. He has been working in the area of intelligent vehicles for more than 20 years, and currently serve as an Associate Editor of IEEE Transaction of Intelligent Vehicles.