Guanxu Long

Papers

Total Citations

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About

Guanxu Long is a researcher whose work centers on intelligent robotics and optimization algorithms, with a particular focus on mobile robot path planning. His most notable contribution is the development of the reformative bat algorithm (RBA), a novel metaheuristic approach that enhances traditional bat-inspired optimization by incorporating the Doppler effect into frequency updates. This innovation, detailed in his highly cited 2022 paper, provides a more adaptive and efficient control mechanism for autonomous navigation in complex environments. The work has garnered 9 citations, reflecting its growing influence among robotics and computational intelligence researchers. Long’s research addresses a critical challenge in robotics—enabling mobile robots to plan safe, collision-free paths in real time—and his algorithmic improvements offer practical solutions for real-world deployment. By bridging bio-inspired computation and robotic control, he contributes to the broader advancement of autonomous systems, making his work a valuable reference for students and engineers exploring path planning, swarm intelligence, and optimization-driven robotics.