Xinshun Ning
Papers
2
Total Citations
13
H-Index
2
About
Xinshun Ning is a researcher specializing in multi-robot systems, path planning, and reinforcement learning, with a focus on overcoming challenges in unknown and obstacle-rich environments. Their major contribution lies in advancing Q-learning algorithms for multi-robot formation control, particularly addressing the complexities of concave obstacles—a notoriously difficult problem in robotics. Ning’s work introduces an improved Q-learning method that enables leader-follower formations to navigate dynamically, enhancing both safety and efficiency. Their most-cited paper (2021, 7 citations) proposes a novel approach that integrates formation maintenance with adaptive path planning, while a closely related study (2021, 6 citations) refines this method for broader applicability. Though early in their career, Ning’s research has already garnered attention for its practical implications in autonomous systems, such as search-and-rescue or warehouse logistics. By merging theoretical reinforcement learning with real-world robotic constraints, Ning offers a scalable solution that reduces computational overhead while improving collision avoidance—a promising step toward robust, decentralized multi-agent coordination. Their work is a valuable resource for students and researchers exploring intelligent robotics and adaptive control.
Research Focus
Key Achievements
Top Papers
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- 2