Papers
5
Total Citations
296
H-Index
5
About
Bing Fu is a robotics researcher whose work spans intelligent motion planning, joint torque sensing, and cooperative multi-agent systems. He is perhaps best known for his 2018 paper on an improved A* algorithm for industrial robot path planning, which achieved notably high success rates and optimized path lengths — a contribution that has garnered over 250 citations and established him as a significant voice in robot navigation and motion planning. His research has since evolved toward enabling safer human-robot interaction, with a focused effort on joint torque sensor design and calibration for compliance control in cooperative robots, lightweight systems, and exoskeletons. His 2021 and 2022 studies address the critical challenge of real-time, accurate torque measurement while minimizing crosstalk error — technical advances that directly support dynamic physical interaction between robots and humans. Earlier in his career, Fu contributed to foundational problems in robot kinematics, proposing an offset modification method for inverse kinematics of manipulators with offset wrists, and explored multi-agent cooperation strategies in competitive robotic environments. Across these diverse threads, Fu's work consistently aims to make robots more intelligent, precise, and safely adaptable to human-centered applications.
Research Focus
Key Achievements
Top Papers
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- 2Design and Calibration of a Joint Torque Sensor for Robot Compliance Control17 citations · 2021
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