Peiwei Zheng
Papers
2
Total Citations
47
H-Index
2
About
Peiwei Zheng is a leading researcher in multi-robot systems and autonomous navigation, with a focus on deep reinforcement learning (DRL) for decentralized coordination. Their most impactful work, "End-to-end Decentralized Multi-robot Navigation in Unknown Complex Environments via Deep Reinforcement Learning" (2019, 42 citations), introduces a novel DRL-based method enabling robot teams to navigate unknown environments while avoiding collisions, maintaining connectivity, and reaching a goal position. This approach eliminates the need for centralized control, allowing each robot to make independent decisions based on local observations, significantly advancing scalability and robustness in swarm robotics. Zheng’s follow-up work, "Connectivity Guaranteed Multi-robot Navigation via Deep Reinforcement Learning" (2019, 5 citations), further refines these techniques to ensure network connectivity under dynamic conditions. Their contributions are pivotal for applications in search-and-rescue, environmental monitoring, and autonomous exploration, where reliable multi-robot coordination is critical. With growing citation impact, Zheng’s research bridges theoretical DRL advances with practical robotic systems, establishing them as a key innovator in decentralized multi-agent navigation.
Research Focus
Key Achievements
Top Papers
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- 2