Yongan Ye

About

Yongan Ye is a researcher in robotics and intelligent control systems, with a primary focus on path planning and optimization algorithms for autonomous mobile robots operating in dynamic environments. His most significant contribution is the development of an innovative hybrid algorithm that combines Particle Swarm Optimization with an Aging Leader and Challengers (ALC-PSO) and Rapidly-exploring Random Tree (RRT) techniques. In his 2014 paper, Ye proposed a novel approach that mimics the RRT concept of root node growth toward a goal point, effectively enhancing the ALC-PSO algorithm's ability to generate optimal, collision-free paths in real-time. This work addresses critical challenges in mobile robot navigation, such as avoiding obstacles in changing surroundings while maintaining computational efficiency. Though his highly specialized paper has garnered 2 citations, it represents a meaningful step forward in merging evolutionary computation with sampling-based planning methods. Ye's research contributes to the broader fields of swarm intelligence and autonomous navigation, offering practical solutions for robotics applications requiring adaptive and efficient path planning.

Research Focus

Key Achievements

Top Papers

1. 1
   Using ALC-PSO algorithm with particle growing method path planning in dynamic environments

   2 citations · 2014

Key Collaborators

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