Papers
4
Total Citations
411
H-Index
3
About
Hong Qu is a leading researcher in the field of mobile robotics, with a primary focus on intelligent path planning, obstacle avoidance, and multi-agent coordination. His most impactful work, an improved genetic algorithm with a co-evolutionary strategy for global path planning of multiple mobile robots, has garnered 235 citations, establishing a foundational approach for coordinating multiple robots in complex environments. Qu is also widely recognized for developing a modified pulse-coupled neural network (MPCNN) model for real-time, collision-free path planning in nonstationary environments, a highly cited contribution (155 citations) that enables robots to navigate dynamic settings using only local lateral connections. More recently, his application of reinforcement learning for mobile robot obstacle avoidance (2018) demonstrates his ongoing commitment to advancing adaptive, intelligent navigation systems. Through these contributions, Qu has significantly shaped the theoretical and practical frameworks for autonomous robot navigation, providing efficient, scalable solutions that remain essential references for researchers and engineers working on multi-robot systems and real-time motion planning.
Research Focus
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Top Papers
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