Anping Lin
Papers
5
Total Citations
174
H-Index
4
About
Anping Lin is a robotics and artificial intelligence researcher whose work centers on multi-robot systems, swarm intelligence, and autonomous navigation in complex environments. His research primarily addresses the challenge of coordinating multiple robots to search for and reach targets in unknown, dynamic environments — a problem of significant practical importance for search-and-rescue, exploration, and autonomous systems applications. Lin's most impactful contributions lie in adapting and hybridizing nature-inspired optimization algorithms for multi-robot coordination. His 2019 papers introducing PSO-FOA hybrid and bat algorithm-based approaches to multi-robot target searching each garnered 57 citations, establishing him as a recognized voice in swarm-intelligence-driven robotics. His 2021 work applying Grey Wolf Optimization (GWO) to cooperative multi-robot search further extended this line of inquiry, accumulating 47 citations. Across these studies, Lin consistently tackles environments where prior maps are unavailable, making his methods particularly relevant to real-world deployment. Beyond search strategies, Lin has made notable contributions to task assignment and path planning, leveraging Self-Organizing Map (SOM) neural networks combined with artificial potential field algorithms to enable efficient, collision-free multi-robot operation. His body of work reflects a sustained commitment to bridging bio-inspired computation with practical autonomous robotics challenges.
Research Focus
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Top Papers
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