Papers
7
Total Citations
32
H-Index
4
About
Renquan Dong’s research lies at the critical intersection of robotic locomotion, terrain adaptation, and rehabilitation engineering. His primary contributions focus on enhancing the stability and obstacle avoidance capabilities of both four-wheeled and quadruped crawling robots operating on complex sloped terrains. Dong has pioneered gait planning strategies that allow robots to recognize and adapt to slope angles, raised terrain, and friction variations, directly addressing the challenges of collision and overturning during emergency maneuvers. His work on foothold optimization using reinforcement learning represents a significant step toward autonomous, intelligent locomotion in unstructured environments. With over 30 cumulative citations across his most-cited papers, Dong’s impact is evident in the growing field of outdoor reconnaissance robotics. Notably, he has also advanced medical robotics by developing fuzzy impedance control and sliding mode controllers for upper-limb rehabilitation robots, improving position tracking efficiency for stroke patients. His 2023 paper on membership function optimization learning strategies marks a key achievement in adaptive control for human-robot interaction. Dong’s dual focus on rugged-terrain mobility and assistive robotics demonstrates a versatile engineering approach, making his work valuable for researchers in both field robotics and rehabilitation technology.
Research Focus
Key Achievements
Top Papers
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