Haoxuan Pan
Papers
1
Total Citations
11
H-Index
1
About
Haoxuan Pan is a researcher at the forefront of multi-robot systems and artificial intelligence, with a primary focus on developing intelligent coordination strategies for robotic teams. His most cited work, "Balancing Efficiency and Unpredictability in Multi-robot Patrolling: A MARL-Based Approach" (2023), addresses a fundamental challenge in security and surveillance robotics: how to design patrol routes that are both efficient—minimizing revisit times to critical areas—and unpredictable, to deter adversarial observation. By leveraging Multi-Agent Reinforcement Learning (MARL), Pan’s approach enables robots to learn collaborative policies that dynamically balance these competing objectives, offering a significant advance over traditional deterministic or purely random patrolling methods. With 11 citations in a short time, this work has already attracted attention from researchers in robotics, security, and autonomous systems. Pan’s contributions are particularly notable for their practical implications in real-world applications such as warehouse security, border surveillance, and environmental monitoring, where adaptive, resilient multi-robot coordination is essential. His research continues to push the boundaries of how autonomous agents can work together under uncertainty.
Research Focus
Key Achievements
Top Papers
- 1