Papers
1
Total Citations
47
H-Index
1
About
Xin Lin is a researcher whose work lies at the intersection of robotics, path planning, and intelligent decision-making systems. His primary research focuses on developing efficient, collision-free navigation strategies for mobile robots operating in complex environments. Lin’s most notable contribution is his 2020 paper, "Path Planning with Improved Artificial Potential Field Method Based on Decision Tree," which has garnered 47 citations. In this work, he ingeniously integrates decision tree logic into the traditional artificial potential field method, overcoming common pitfalls like local minima and goal unreachability. This hybrid approach significantly enhances the safety and reliability of autonomous navigation, offering a more robust solution for real-world robotic applications. By improving how robots perceive and react to obstacles, Lin’s research directly advances the fields of autonomous vehicles, warehouse logistics, and service robotics. His work is particularly valuable for students and engineers seeking practical, implementable algorithms for dynamic path planning. With a clear focus on bridging theoretical optimization and real-world deployment, Xin Lin continues to contribute meaningful innovations to the growing domain of intelligent mobile systems.
Research Focus
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Top Papers
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