Pengming Zhu
Papers
8
Total Citations
177
H-Index
5
About
Pengming Zhu is a leading researcher in multi-robot systems and autonomous navigation, with a focus on integrating deep reinforcement learning (DRL) and graph neural networks to solve complex coordination and control problems. His most influential work, "Multi-Robot Flocking Control Based on Deep Reinforcement Learning" (76 citations), pioneered the use of DRL for flocking in dynamic obstacle environments, eliminating the need for traditional model-based approaches. He further advanced crowd navigation by combining DRL with online planning (69 citations), enabling robots to safely and efficiently traverse human-filled spaces. Zhu has also made notable contributions to multimodal robotics, designing an aerial/ground dual-modal robot (2021) and a novel quadrotor tilting hybrid robot (2023) that overcome the endurance and terrain limitations of single-locomotion systems. His research on imitation learning and graph neural networks (2021, 2024) addresses critical challenges in swarm robustness under restricted communications, while his consensus-based task allocation algorithm (2024) optimizes multi-robot performance under payload constraints. With over 175 total citations and a growing portfolio of high-impact work, Zhu is shaping the future of intelligent, resilient multi-robot systems for real-world exploration and task execution.
Research Focus
Key Achievements
Top Papers
- 1Multi-Robot Flocking Control Based on Deep Reinforcement Learning76 citations · 2020
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