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About

Weiping Fu is a leading researcher in mobile robotics, autonomous navigation, and human-robot collaboration, with a particular focus on ensuring safe and intelligent robot behavior in complex environments. Fu’s early work introduced a novel line space voting method for vanishing-point detection in general road images (24 citations), a critical component for visual navigation systems in autonomous mobile robots. Building on this, Fu developed a global path planning method using Teaching-Learning-Based Optimization (17 citations), demonstrating innovative applications of swarm intelligence to robotics. More recently, Fu has pioneered research on collaborative robot (cobot) safety, proposing a motion planning algorithm based on behavioral dynamics that quantitatively assesses human psychological reactions (12 citations). Fu also advanced human-robot interaction by integrating intuitionistic fuzzy set theory and game theory for cobot action decision-making (9 citations), addressing the bounded rationality of human collaborators. Notable achievements include work on visual-language navigation that reduces dependence on geographic information systems, and a multimodal deep learning method for fault detection in ultrahigh voltage substations using inspection robots. With over 77 total citations, Fu’s research continues to shape the future of intelligent, human-aware robotic systems.