Yuefan Chen

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About

Yuefan Chen is a researcher focused on advancing autonomous navigation for mobile robots, with particular expertise in bio-inspired and swarm intelligence algorithms for path planning. Their major contributions lie in enhancing the performance of two foundational optimization methods: ant colony optimization and particle swarm optimization. In their 2021 work on an improved ant colony algorithm, Chen addressed critical limitations of the traditional approach—namely, prolonged early search times, susceptibility to local optima, and slow convergence—by introducing novel modifications to the evaluation function. This paper has garnered 12 citations, reflecting its practical relevance. Building on this, Chen’s 2022 study on robot path planning using improved particle swarm optimization tackled the algorithm’s weak local search ability and tendency to stagnate in local optima, innovatively incorporating an inflection point factor to guide more efficient trajectories. With 6 citations, this work further demonstrates Chen’s systematic approach to refining heuristic methods for real-world robotics applications. Together, these contributions establish Chen as a thoughtful contributor to the field of intelligent robotics, offering tangible improvements that help robots navigate complex environments more reliably and efficiently.