WU Qing-quan

China Southern Power Grid (China), Guangdong University of Technology

Papers

Total Citations

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About

Dr. Wu Qing-quan is a researcher specializing in intelligent path planning and autonomous navigation, with a particular focus on dynamic and unstructured environments. His work integrates computational intelligence techniques—including fuzzy logic, artificial potential fields, and swarm-based optimization—to address the challenge of real-time, globally optimized route generation. Dr. Wu’s major contributions lie in developing hybrid frameworks that combine fuzzy neural networks and ant colony systems to enable collision-free, shortest-path solutions under changing conditions. His 2016 paper on combining artificial potential fields with fuzzy neural networks has garnered 4 citations, while his earlier 2009 work on ant colony system-based path planning has received 2 citations. Though his citation counts are modest, his research represents a meaningful step toward bridging heuristic optimization with adaptive control for mobile robotics. Dr. Wu’s work is particularly relevant for applications in autonomous vehicles, drone navigation, and intelligent transportation systems, where real-time decision-making in unpredictable settings is critical. His contributions continue to inform the development of more responsive and efficient path planning algorithms.