Zhibin Mo
Papers
3
Total Citations
12
H-Index
2
About
Zhibin Mo is a robotics researcher focused on advancing human–multi-robot coordination and autonomous navigation. His work centers on reinforcement learning, behavioral control, and distributed formation strategies for mobile robots operating in uncertain environments. In his most cited paper (2022, 8 citations), Mo introduced a reinforcement learning task supervisor with memory, significantly reducing decision-making time and tracking errors in human–multi-robot coordination systems—a critical step toward safer, more efficient human-robot teams. He further developed a fully actuated behavioral control scheme for omnidirectional mobile robots (2024, 2 citations), enabling robust formation control despite uncertain dynamics and external disturbances. Mo has also contributed to obstacle detection and avoidance methods for indoor mobile robots, addressing real-world navigation challenges. His work bridges theoretical control frameworks with practical multi-robot applications, earning recognition for improving system responsiveness and reliability. With a growing citation impact, Mo’s research is shaping the future of cooperative robotics, particularly in scenarios requiring seamless human intervention and adaptive multi-robot coordination.
Research Focus
Key Achievements
Top Papers
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