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About

Shih-An Li is a prolific robotics and intelligent systems researcher whose work spans mobile robot control, path planning, computer vision, and human-robot interaction. Over nearly two decades, Li has made substantial contributions to the field of autonomous mobile robotics, accumulating over 170 citations across his most recognized publications. Li's earliest and most influential work focused on applying evolutionary computation to fuzzy control systems. His 2007 and 2008 papers introduced genetic algorithm (GA) and particle swarm optimization (PSO) techniques to automatically tune fuzzy membership functions for two-wheeled mobile robots, eliminating tedious manual controller design — work that together garnered nearly 70 citations. He subsequently expanded into path planning, developing hybrid global-local algorithms using Voronoi graphs, and obstacle avoidance frameworks leveraging HyperOmni Vision and dynamic window approaches. More recently, Li has embraced cutting-edge artificial intelligence paradigms. His 2023 work on deep neural network-based voice interaction for service robots demonstrates his adaptability to emerging technologies, earning 35 citations. His 2024 research on hybrid centralized-decentralized deep reinforcement learning for multi-agent path-finding further highlights his forward-looking research agenda. Collectively, Li's body of work reflects a consistent commitment to making autonomous robots more intelligent, adaptable, and practically deployable in real-world environments.