Yongbo Song

Chinese Academy of Sciences, Shandong Institute of Automation, Beijing Institute of Technology

Papers

Total Citations

H-Index

About

Yongbo Song is a robotics researcher whose work focuses on advancing autonomous manipulation and perception systems, particularly for underwater and industrial environments. His key research areas include robotic grasping, hand-eye calibration, and vision-based perception for autonomous systems. Song made notable contributions to the study of robotic grasping, where he explored the relationship between cage grasping and form-closure grasps, providing insights that enable low-cost, reliable manipulation using one-degree-of-freedom grippers—a critical need in manufacturing automation. His work on simultaneous hand-eye calibration for hybrid eye-in-hand/eye-to-hand systems has improved the accuracy and speed of object localization and grasping in autonomous exploration equipment like remotely operated vehicles (ROVs). Additionally, Song has advanced underwater vision by incorporating structural constraints into image matching algorithms, enhancing the perception capabilities of underwater robots. He also developed an omnidirectional vision system for environment perception, featuring a modular design with five degrees of freedom. With his most cited paper accumulating 19 citations, Song's research continues to impact the fields of robotic manipulation, calibration, and underwater perception, supporting the development of more intelligent and autonomous robotic systems.