l 2022年11月7日（周一） 腾讯会议：541-623-468 密码：221107
报告嘉宾：Prof. Jizhong Xiao, The City College of New York
报告题目：Toward Autonomous Wall-climbing Robots for Inspection of Concrete Infrastructure
Numerous civil structures (bridges, dams, tunnels, highways, buildings, etc.) in USA and around the world are reaching their life expectancy, and thus have strong needs for routine inspection and maintenance to ensure sustainability. In addition to visual inspection of surface flaws, inspectors are often required to detect subsurface defects (e.g., delamination and voids) using nondestructive evaluation (NDE) instruments, such as ground penetration radar (GPR) and impact echo/impact sounding devices, in order to determine the structural integrity of concrete structures. Dr. Jizhong Xiao’s group has developed four generations of wall-climbing robots for NDE inspection of civil infrastructure. These robots combine the advantages of aerodynamic attraction and suction to achieve a desirable balance of strong adhesion and high mobility. For example, Rise-Rover with two drive modules can carry up to 450 N payload, and GPR-Rover can carry a small GPR antenna for subsurface flaw detection and utility survey on concrete structures. These robots can reach difficult-to-access areas, take close-up pictures, record and transmit NDE data to a host computer for further analysis. They can potentially make bridge inspection faster, safer, and cheaper without affecting traffic flow on roadways. This presentation will review the recent development of smart and autonomous wall-climbing robots and NDE data analysis to realize automated inspection of civil infrastructure with minimal human intervention.
Dr. Jizhong Xiao is a Professor at the Department of Electrical Engineering of the City College of New York (CCNY), the flagship campus of City University of New York (CUNY), as well as a doctoral faculty member of the Computer Science Ph.D. program at the CUNY Graduate Center. He received his Ph.D. degree from the Michigan State University in 2002, Master of Engineering degree from Nanyang Technological University, Singapore in 1999, M.S, and B.S. degrees from the East China Institute of Technology, Nanjing, China, in 1993 and 1990, respectively.
He started the Robotics Research Program at the City College in 2002 and is the Founding Director of CCNY Robotics and Intelligent Systems Laboratory and the Center for Perceptual Robotics, Intelligent Sensors and Machines (PRISM Center). With his leadership, the robotics research has become one of the most active and well-funded research directions at the EE department of CCNY. His current research interests include robotics and control, cyber-physical systems, autonomous navigation and 3D simultaneous localization and mapping (SLAM), wall-climbing robots for non-destructive evaluation of infrastructure, assistive technology, multi-agent systems and swarm robotics. He has published more than 170 research articles in peer reviewed journal and conferences. Dr. Xiao received the U.S. National Science Foundation CAREER Award in 2007, the CCNY Outstanding Mentoring Award in 2011, and Humboldt Research Fellowship for Experienced Researchers from the Alexander von Humboldt Foundation, Germany in 2013~2015, and Dean’s Award for Excellence in 2019.
He is the founder and senior technical advisor of InnovBot LLC, a CUNY spin-off company dedicated to the R&D and commercialization of special robots in non-destructive inspection of infrastructure and wind turbine blades.
报告嘉宾：Professor Jingang Yi, Department of Mechanical and Aerospace Engineering, Rutgers University, Piscataway, NJ 08854
报告题目： Learning-based Control of Underactuated Balance Robots
Underactuated balance robots have more degrees of freedom than the number of control inputs and they perform the balancing and tracking tasks simultaneously, such as rotational inverted pendulums, bicycles and bipedal walkers, etc. The balancing task requires the robot to maintain its balance around unstable equilibrium points, while the tracking task requires following desired trajectories. In this talk, I first review the model-based control design of the underactuated balance robots. Balance equilibrium manifold is used to capture the external trajectory tracking and internal balance performance. I will then present a machine learning model-based control for underactuated balance robots. Gaussian process is used to obtain the estimation of the systems dynamics and the learning process is obtained without need of prior physical knowledge nor successful balance demonstrations. Additional attractive property of the design includes the guaranteed stability and closed-loop performance. Experiments from a Furuta pendulum and a bikebot are used to demonstrate the performance of the learning-based control design.
Professor Jingang Yi received the B.S. degree in electrical engineering from Zhejiang University in 1993, the M.Eng. degree in precision instruments from Tsinghua University in 1996, and the M.A. degree in mathematics and the Ph.D. degree in mechanical engineering from the University of California, Berkeley, in 2001 and 2002, respectively. He is currently a Full Professor in mechanical engineering and a Graduate Faculty member in electrical and computer engineering at Rutgers University. His research interests include human-robot interactions and assistive robotics, autonomous robotic and vehicle systems, dynamic systems and control, mechatronics, automation science and engineering, with applications to biomedical, transportation and civil infrastructure systems. Prof. Yi is a Fellow of American Society of Mechanical Engineers (ASME) and a Senior Member of IEEE. He has received several awards, including the 2017 Rutgers Chancellor’s Scholars, 2014 ASCE Charles Pankow Award for Innovation, the 2013 Rutgers Board of Trustees Research Fellowship for Scholarly Excellence, and the 2010 NSF CAREER Award. He has coauthored several best papers in IEEE Transactions on Automation Science and Engineering and at IEEE/ASME AIM, ASME DSCC, and IEEE ICRA, etc. He serves as a Senior Editor for IEEE Robotics and Automation Letters and IEEE Transactions on Automation Science and Engineering, and an Associate Editor for International Journal of Intelligent Robotics and Applications. He also served in editorial board of IFAC journals Control Engineering Practice, Mechatronics, IEEE/ASME Transactions on Mechatronics, IEEE Transactions on Automation Science and Engineering, IEEE Robotics and Automation Letters, and ASME Journal of Dynamic Systems, Measurement and Control.
报告嘉宾：Prof. Dezhen Song, Texas A&M University
报告题目：A Few Attempts to Improve Robustness of Visual SLAM
When a camera is employed as the primary sensor to perform simultaneous localization and mapping (SLAM) task for a robot or a mobile device, it is often referred to as the visual SLAM approach. Visual SLAM has seen many applications including augmented reality, autonomous driving, and service robotics due to its low cost in sensory hardware and small footprint. It is vital part of navigation and scene reconstruction. However, visual SLAM still suffers from robustness issue due to its reliance on the continuously successful image matching process. Due to lighting, camera perspective, and feature distribution, vSLAM algorithms still have non negligible failure rate. Here we present a few attempts that our lab has tried to attack the robustness issue from multiple angles: exploiting complex feature, spatial knowledge sharing, better robust estimation, and improvement of sparse optimization solver. We present those approaches and hope to encourage discussion and attention to the robustness issue which is the main hurtle in many real-world applications.
Dezhen Song is a Professor and Associate Department Head for Academics with Department of Computer Science and Engineering, Texas A&M University, College Station, Texas, USA. Song received his Ph.D. in 2004 from University of California, Berkeley; MS and BS from Zhejiang University in 1995 and 1998, respectively. Song's primary research area is robot perception, networked robots, visual navigation, automation, and stochastic modeling. He received NSF Faculty Early Career Development (CAREER) Award in 2007. From 2008 to 2012, Song was an associate editor of IEEE Transactions on Robotics (T-RO). From 2010 to 2014, Song was an Associate Editor of IEEE Transactions on Automation Science and Engineering (T-ASE). Song was a Senior Editor for IEEE Robotics and Automation Letters (RA-L) from 2017 to 2021. He is also a multimedia Editor and chapter author for Springer Handbook of Robotics. Dezhen Song has been PI or Co-PI on more than $17 million in grants including more than $5.6 million from NSF. His research has resulted in one monograph and more than 130 refereed conference and journal publications.