Shengjun Chen

South China University of Technology, Guangdong University of Technology

Papers

Total Citations

H-Index

About

Shengjun Chen is a robotics researcher whose work focuses on motion planning and control for climbing robots and human-robot interaction. His key research areas include biped climbing robots for navigating spatial trusses, collision-free trajectory planning, and intuitive robotic manipulation using vision-based systems. Chen’s major contributions lie in developing sampling-based algorithms, such as Bi-RRT, to enable single-step, collision-free climbing motions for biped pole-climbing robots (BiPCRs) equipped with dual grippers—advancing autonomous inspection and maintenance in complex structures like trusses and trees. His work on Kinect-based hand tracking (2015) introduced a real-time method for translating human hand movements into end-effector control, bridging intuitive human-robot interfaces. Additionally, his off-line programming approach using DXF files from 3D models (2013) simplified complex trajectory generation for industrial robots. With over 50 citations across his most-cited papers, Chen’s research has practical implications for robotics in hazardous environments and manufacturing. His achievements include pioneering collision-free motion planning for climbing robots and developing accessible robotic programming methods, making him a notable contributor to the fields of robotic locomotion and human-robot collaboration.