Papers
8
Total Citations
197
H-Index
6
About
Hongwei Tang is a robotics and autonomous systems researcher whose work spans swarm intelligence, multi-robot coordination, and motion planning in unknown environments. His most significant contributions center on applying bio-inspired optimization algorithms — including Particle Swarm Optimization (PSO), Fruit Fly Optimization (FOA), Bat Algorithm, and Grey Wolf Optimization (GWO) — to enable cooperative multi-robot target searching without prior environmental knowledge. His 2019 papers introducing PSO-FOA hybrid and bat algorithm-based approaches each garnered 57 citations, while his 2021 GWO-based cooperative method has accumulated 47 citations, reflecting sustained community interest in his framework designs. Tang's earlier foundational work (2011) pioneered real-time motion planning by integrating fuzzy logic, virtual force field, and boundary-following techniques for autonomous robot navigation using sonar sensing. He has also advanced multi-robot task assignment through improved Self-Organizing Map (SOM) neural networks combined with artificial potential fields, and extended his research into practical applications including power line deicing robots and wireless sensor network deployment for coal mine safety monitoring. Collectively, his body of work demonstrates a coherent research vision: bridging computational intelligence with practical autonomous robotics challenges across dynamic, unstructured environments.
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