Xiaolin Ai
Papers
2
Total Citations
53
H-Index
2
About
Xiaolin Ai is a leading researcher in multiagent systems and intelligent control, with a focus on integrating deep reinforcement learning with model-based paradigms to solve complex coordination challenges. Their most cited work, a 2023 paper on multiagent formation control with collision avoidance, has garnered 29 citations and introduces a novel hybrid approach that combines data-driven learning with traditional control theory to generate safe, collision-free strategies in highly dynamic environments. Ai’s 2022 study on distributed coordinated tracking control for multi-manipulator systems under intermittent communications, with 24 citations, addresses critical real-world constraints where communication links are unreliable. These contributions demonstrate Ai’s ability to bridge theoretical frameworks and practical applications, advancing the field of autonomous multiagent navigation and robotic manipulation. Their work is particularly impactful for students and researchers exploring robust, scalable solutions in swarm robotics and cooperative control, offering a blueprint for tackling uncertainty and communication limitations in multiagent systems.
Research Focus
Key Achievements
Top Papers
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