Papers
3
Total Citations
34
H-Index
3
About
Zongze Wu is at the forefront of advancing autonomous multirobot systems and intelligent trajectory planning. His research primarily focuses on solving critical challenges in multirobot coordination and learning-based control for mobile robots. Wu’s most impactful contribution is his work on the multirobot task assignment problem with deadlines, where he developed efficient performance impact algorithms enabling distributed heterogeneous robots to maximize successful search and rescue missions while minimizing total service time—a paper that has already garnered 24 citations since 2024. He has also pioneered novel approaches to trajectory planning, introducing a Lyapunov-based reinforcement learning framework with implicit policy for mobile robots, and a Lyapunov-based imitation learning framework for wheeled vehicles. These works, each with 5 citations in 2025, address persistent issues in data efficiency, safety, convergence, and generalization that have long challenged the field. By integrating rigorous control-theoretic guarantees with modern learning methods, Wu’s research bridges the gap between theoretical stability and practical deployment, offering safer, more reliable solutions for autonomous unmanned systems in real-world applications like search and rescue and autonomous navigation.
Research Focus
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