Papers
51
Total Citations
1,496
H-Index
17
About
Wenzheng Chi is a prominent robotics researcher whose work centers on motion planning, autonomous navigation, and human-robot interaction. He is best known for his pioneering contributions to sampling-based path planning algorithms, particularly innovations built upon the Rapidly-exploring Random Tree (RRT) framework. His landmark paper "Neural RRT*: Learning-Based Optimal Path Planning" (2020) has garnered over 530 citations, demonstrating how deep learning can dramatically accelerate convergence and reduce computational overhead in classical planning algorithms. Chi has consistently pushed the boundaries of RRT-based methods, developing risk-aware variants for human-robot coexisting environments, bidirectional search strategies, and Voronoi diagram-guided heuristics to overcome challenging trap-space scenarios in mobile robot navigation. Beyond path planning, his research extends to autonomous exploration in unknown environments, semantic-aware informative planning, and gait-recognition-based human following for service robots. His 2024 survey on autonomous and multi-robot collaboration reflects his growing influence in coordinated robotic systems. With a cumulative citation count exceeding 1,100, Chi's body of work offers students and researchers an essential foundation for understanding modern intelligent robot navigation and planning.
Research Focus
Key Achievements
Top Papers
- 1Neural RRT*: Learning-Based Optimal Path Planning533 citations · 2020
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- 3Risk-DTRRT-Based Optimal Motion Planning Algorithm for Mobile Robots82 citations · 2018
- 4Autonomous Robotic Exploration by Incremental Road Map Construction78 citations · 2019
- 5A Gait Recognition Method for Human Following in Service Robots69 citations · 2017
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- 7Robot Path Planning via Neural-Network-Driven Prediction42 citations · 2021
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