Wen-Kai Fang
Papers
1
Total Citations
35
H-Index
1
About
Wen-Kai Fang is a leading researcher in intelligent robotics and autonomous navigation, with a focus on path planning for search and rescue operations. His most cited work, "Improved ACO algorithm fused with improved Q-Learning algorithm for Bessel curve global path planning of search and rescue robots" (2024), has garnered 35 citations, showcasing its immediate impact on the field. Fang’s major contribution lies in hybridizing ant colony optimization (ACO) with Q-learning, a reinforcement learning technique, to generate smoother, more efficient Bessel curve paths for robots operating in complex, dynamic environments. This fusion addresses critical challenges in real-time obstacle avoidance and energy efficiency, directly enhancing the capabilities of search and rescue robots in disaster scenarios. His work bridges classical swarm intelligence with modern machine learning, offering a novel paradigm for adaptive path planning. Fang’s research not only advances theoretical algorithms but also provides practical solutions for life-saving applications, making him a notable figure in robotics. His achievements underscore a commitment to developing robust, intelligent systems that operate reliably under uncertainty, inspiring future work in autonomous navigation and human-robot interaction.
Research Focus
Key Achievements
Top Papers
- 1