Papers
5
Total Citations
53
H-Index
5
About
Xu Li is a robotics and control systems researcher whose work spans multi-robot coordination, autonomous vehicle systems, and advanced robot kinematics. His most influential contribution, a 2015 study on leader-follower formation control and obstacle avoidance in multi-robot systems, earned 27 citations and introduced a novel hybrid approach combining closed-loop control with Artificial Potential Field methods, providing a robust framework for coordinating robot teams in complex environments. Building on his expertise in intelligent control, Li has made notable strides in adaptive systems, developing a fuzzy logic-based speed control method for electromagnetic direct drive vehicle robot drivers that addresses persistent challenges of tracking error and mileage deviation. His interests extend into machine learning applications for robotics, including reinforcement learning for two-wheeled robot self-balancing and efficient inverse kinematics solutions for hyper-redundant robots — both reflecting a forward-looking integration of computational intelligence with mechanical systems. Li's editorial involvement in a special issue on wireless sensor and robot networks further demonstrates his broader engagement with the research community. Collectively, his work advances the intersection of autonomous control, adaptive algorithms, and practical robotics engineering.
Research Focus
Key Achievements
Top Papers
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- 4Model Learning for Two-Wheeled Robot Self-Balance Control5 citations · 2019
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