Papers
31
Total Citations
552
H-Index
13
About
Zhengcai Cao is a robotics and control systems researcher whose work spans motion planning, bio-inspired locomotion, fault detection, and multi-robot coordination. His research has made notable contributions to the fields of snake-like robot control and autonomous navigation, combining advanced computational methods with real-world robotic challenges. Cao's most cited work (76 citations) presents an efficient RRT-based framework for kinodynamic motion planning in wheeled robots, offering a practical alternative to computationally expensive steer functions. Complementing this, his investigations into snake-like robots — encompassing neuro-optimal control with experience replay (72 citations), variable stiffness actuation (57 citations), and central pattern generator-based locomotion optimization (31 citations) — demonstrate a sustained effort to push the boundaries of bio-inspired robotics in complex environments. His work on finite-frequency fault detection for T-S fuzzy systems (52 citations) reflects additional depth in robust control theory. More recently, Cao has expanded into multi-agent systems and computer vision, with contributions to multi-object tracking under occlusion (39 citations), collaborative multi-robot hunting using Voronoi cells (18 citations), and autonomous cooperative mapping (16 citations). Collectively, his portfolio — accumulating over 400 citations — marks him as a versatile and impactful contributor to intelligent robotics and autonomous systems research.
Research Focus
Key Achievements
Top Papers
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- 7Direction Control and Adaptive Path Following of 3-D Snake-Like Robot Motion29 citations · 2021
- 8Distributed Fusion-Based Policy Search for Fast Robot Locomotion Learning23 citations · 2019
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