Papers
10
Total Citations
340
H-Index
8
About
Lin Zhang is a robotics researcher whose work centers on autonomous navigation, motion planning, and intelligent control systems for mobile and multi-robot platforms. With a career spanning nearly a decade, Zhang has made significant contributions to advancing robot autonomy in complex, real-world environments. Zhang's most impactful work focuses on path planning algorithms, most notably a 2020 study introducing an improved localized Particle Swarm Optimization method for mobile robots, which has garnered 110 citations and addressed long-standing limitations like premature convergence and local minima. Complementing this, Zhang has applied deep reinforcement learning to both indoor navigation and decentralized multi-robot cooperative transportation, the latter accumulating 65 citations and demonstrating how autonomous teams can coordinate without centralized control. More recently, Zhang has pushed into safety-critical robotics, developing sim-to-real reinforcement learning pipelines for cable-driven parallel robots navigating dynamic obstacles, and safe motion planning strategies for autonomous mobile robots operating alongside uncontrollable agents. Additional work spanning sensor fusion, human intention prediction, and RO/RO terminal vehicle transfer robots reflects Zhang's broad yet cohesive vision: creating robots that are adaptive, safe, and practically deployable across industrial and service environments.
Research Focus
Key Achievements
Top Papers
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- 3Path planning for indoor Mobile robot based on deep learning53 citations · 2020
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- 6Robot navigation based on improved A* algorithm in dynamic environment16 citations · 2021
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- 9A Preliminary Study on a Robot's Prediction of Human Intention7 citations · 2017
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