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About

Fangxing Li is a robotics researcher whose work centers on autonomous navigation, perception, and robotic manipulation for construction and industrial applications. His most significant contributions lie in developing intelligent systems for rebar-tying robots, where he pioneered a robotic binding method using active perception and planning (48 citations). Li has also made substantial advances in mobile robot localization, creating the LCPF particle filter SLAM system (24 citations) that improves global map consistency for long-term indoor navigation, and a robust vision-lidar fusion system that maintains localization accuracy under severe occlusion (16 citations). His research extends to self-balancing robots, where he developed sliding mode control for coaxial designs (11 citations), and to path planning with RimJump, an edge-based algorithm for finding strict shortest paths in 2D maps (8 citations). More recently, Li has explored biomedical microrobotics, enhancing swimming performance of magnetic helical microrobots through surface microstructure modification (5 citations). With over 150 total citations across his publications, Li’s work bridges fundamental robotics challenges—localization, mapping, and control—with practical applications in construction automation and biomedical engineering, demonstrating a consistent focus on creating robust, real-world deployable robotic systems.