This book explores peg-in-hole assembly strategies to study robotic intelligent assembly. It presents several state-of-the-art principles for peg-in-hole assembly strategies, supported by experimental evidence. In pursuit of theoretical innovation, the author summarizes their research on robotic intelligent assembly over the past decade, highlighting the limitations of model-based algorithms in complex assembly environments and the importance of data efficiency for learning-based algorithms. Each algorithm is supported by extensive experimentation and results demonstrating its effectiveness. A…mehr
This book explores peg-in-hole assembly strategies to study robotic intelligent assembly. It presents several state-of-the-art principles for peg-in-hole assembly strategies, supported by experimental evidence. In pursuit of theoretical innovation, the author summarizes their research on robotic intelligent assembly over the past decade, highlighting the limitations of model-based algorithms in complex assembly environments and the importance of data efficiency for learning-based algorithms. Each algorithm is supported by extensive experimentation and results demonstrating its effectiveness. A review of research ideas provides readers with a comprehensive understanding of the progress made in this field. This monograph is intended for undergraduate and postgraduate students interested in robotic intelligent assembly, researchers studying robotic intelligent assembly algorithms, and electronic, mechanical, and computer engineers engaged in industrial robot-assisted assembly.
Jing Xu (Member, IEEE) received the B.E. degree in mechanical engineering from the Harbin Institute of Technology, Harbin, China, in 2003, and the Ph.D. degree in mechanical engineering from Tsinghua University, Beijing, China, in 2008. He was a post-doctoral researcher with the Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI, USA. He is currently an associate professor at the Department of Mechanical Engineering, Tsinghua University. Dr. Xu focuses on the research of vision-guided manufacturing, image processing, and intelligent robotics. Since 2008, Dr. Xu has published over 100 peer-reviewed technical papers in international journals and conferences and authorized over 50 invention patents. He is a member of IEEE and served as a deputy editor-in-chief of Robotica and an assistant vice chairman of IEEE Robotics and Automation Society. He received two Best Conference Paper Awards at the IEEE International Conference on Robotics and Biomimetics (IEEE ROBIO 2015) and IEEE International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (IEEE-CYBER 2015), respectively. Hao Su is an associate professor of Computer Science and Engineering in the University of California, San Diego. He is affiliated with the Contextual Robotics Institute and Center for Visual Computing. He served on the program committee of multiple conferences and workshops on computer vision, computer graphics, and machine learning. He is the area chair of ICCV'19, CVPR'19, senior program chair of AAAI'19, IPC of Pacific Graphics'18, program chair of 3DV'17, publication chair of 3DV'16, and chair of various workshops at CVPR, ECCV, and ICCV. He is also invited as a keynote speaker at workshops and tutorials in NIPS, 3DV, CVPR, RSS, ICRA, S3PM, etc. Rui Chen (Member, IEEE) is currently a research assistant professor in the Department of Mechanical Engineering, Tsinghua University. He received the Ph.D. degree in mechatronical engineering and the B.E. degree in mechanical engineering in 2020, 2014 from Tsinghua University, Beijing, China. His research interests include three-dimensional computer vision, tactile sensing, and robot learning. He serves as area chair of CVPR 2025. Zhimin Hou earned his Bachelor of Engineering (B.E.) degree in Mechanical Engineering from Tongji University, Shanghai, China, in 2016. He continued his academic journey by completing a Master's degree in Mechanical Engineering from Tsinghua University, Beijing, China, in 2019. In 2018, he studied reinforcement learning in Computing Science at the University of Alberta as a visiting student. He earned a Ph.D. degree in the Department of Biomedical Engineering at the National University of Singapore, Singapore, in 2023. His research interests include reinforcement learning, physical human-robot interaction, and robot physical training.
Inhaltsangabe
Introduction.- Model based strategies.- Implementation.- Learning based strategies theoretical Analysis.- Learning based Implementation.- Conclusion.
