Yongjun Wang
Papers
2
Total Citations
20
H-Index
2
About
Yongjun Wang is a robotics researcher whose work focuses on the critical challenges of safe and efficient physical human-robot interaction. His primary research areas include collision detection for collaborative robots and advanced path planning for robotic arms. Wang’s most significant contribution is the development of a novel sliding mode momentum observer for collision detection, which addresses the fundamental safety requirement of robots working alongside humans. This approach offers an economically feasible solution by eliminating the need for additional sensors, overcoming the traditional trade-off between collision sensitivity and robustness. His work in this area has garnered attention, with his 2022 paper receiving 18 citations. More recently, Wang has advanced robot autonomy through his research on obstacle avoidance path planning, where he improved the Rapidly-exploring Random Tree (RRT) algorithm to overcome its limitations of non-smooth paths and low search efficiency. By enhancing adaptability to dynamic environments, his work contributes to more practical and reliable robotic systems for real-world applications. Wang’s research continues to push the boundaries of safe and intelligent robot motion, making him a notable figure in the field of collaborative robotics.
Research Focus
Key Achievements
Top Papers
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- 2