Zhiwen Zeng
Papers
23
Total Citations
596
H-Index
11
About
Zhiwen Zeng is a leading researcher in multi-robot systems, distributed coordination control, and deep reinforcement learning (DRL) for autonomous navigation. His work bridges the gap between theoretical control frameworks and practical, intelligent robotics. Zeng’s most influential contribution is his 2016 paper on multi-agent distributed coordination control via graph theory, which has garnered 117 citations and serves as a foundational reference for the field. He pioneered the application of DRL to complex multi-robot tasks, notably in flocking control (76 citations) and target encirclement with collision avoidance (61 citations). His innovative HGAT-DRL framework for robot crowd navigation (40 citations) was developed in response to COVID-19, enabling robots to safely operate in human-populated environments. Zeng has also advanced dynamic task allocation for exploration and destruction (65 citations) and high-speed trajectory tracking using model predictive control (24 citations). His distributed encirclement control algorithms (17 citations) and work on intelligent soccer-playing robots for RoboCup Middle-Size League (15 citations) demonstrate his commitment to real-world, dynamic applications. With over 500 total citations, Zeng’s research continues to shape the future of autonomous, cooperative robotic systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2Multi-Robot Flocking Control Based on Deep Reinforcement Learning76 citations · 2020
- 3
- 4Multi-Robot Dynamic Task Allocation for Exploration and Destruction65 citations · 2019
- 5
- 6Navigating Robots in Dynamic Environment With Deep Reinforcement Learning40 citations · 2022
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- 9
- 10The design of an intelligent soccer-playing robot15 citations · 2016