Papers
2
Total Citations
14
H-Index
2
About
Le Gu’s research focuses on intelligent robot navigation and pedestrian behavior analysis in dynamic, crowded public spaces. His major contributions lie in developing computational models that enable robots to safely and efficiently navigate complex human environments, such as train stations and hospitals. In his highly cited 2021 work, Gu proposed a method for detecting abnormal pedestrian trajectories in dynamic crowds, addressing a critical security challenge by helping inspection robots recognize potentially hazardous behavior through enhanced visual feature analysis. This work has garnered 11 citations, reflecting its relevance to public safety robotics. His 2020 study introduced a navigation probability map based on an influencer recognition model, which allows robots to understand and predict pedestrian movement patterns, thereby improving path planning in rapidly changing environments. By tackling the core problem of robot navigation amidst unpredictable human motion, Gu’s research bridges computer vision, crowd dynamics, and autonomous systems. His work is particularly valuable for developing safer, more responsive service robots in high-density public venues, making him a notable contributor to the field of human-aware robot navigation.
Research Focus
Key Achievements
Top Papers
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