Feilong Li
Papers
1
Total Citations
22
H-Index
1
About
Feilong Li is a researcher whose work lies at the intersection of robotics, artificial intelligence, and autonomous navigation. His most recognized contribution is the development of the Evolutionary Artificial Potential Fields (EAPF) approach for mobile robot path planning, a method that addresses a fundamental limitation of traditional potential field techniques: the local minimum problem. By integrating evolutionary algorithms into the APF framework, Li’s work enables robots to escape dead ends and find more efficient, collision-free paths in complex environments. His 2013 paper on this topic has garnered 22 citations, establishing a foundation for subsequent advances in adaptive and intelligent navigation systems. Li’s research is particularly relevant for autonomous mobile robots operating in dynamic or unpredictable settings, such as warehouse logistics, search-and-rescue missions, and service robotics. His contributions continue to influence the design of robust, real-time path planning algorithms, and his work is frequently referenced by engineers and researchers seeking to improve the autonomy and reliability of robotic systems.
Research Focus
Key Achievements
Top Papers
- 1