Jyun-Fu Jiang

About

Jyun-Fu Jiang is a researcher in robotics and autonomous systems, with a primary focus on path planning and optimization algorithms for mobile robots operating in dynamic environments. His most notable contribution is the development of the ALC-PSO (Aging Leader and Challengers Particle Swarm Optimization) algorithm, which he innovatively combined with Rapidly-exploring Random Tree (RRT) concepts to create a particle growing method for optimal path planning. This work, published in 2014, addresses the critical challenge of enabling robots to navigate safely and efficiently through changing surroundings by mimicking the RRT root node growth toward a goal point. While his citation count remains modest, Jiang's research represents a meaningful step in bridging swarm intelligence with traditional path planning techniques, offering a novel approach to real-time navigation problems. His work is particularly relevant for students and researchers exploring hybrid algorithms that leverage the exploration capabilities of PSO with the structured growth of RRT, providing a foundation for further advancements in autonomous mobile robot navigation and obstacle avoidance.

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

Contact & Links