Zhentao Fan
Papers
1
Total Citations
2
H-Index
1
About
Zhentao Fan is a researcher in robotics and computational intelligence, whose work focuses on enhancing autonomous navigation through advanced optimization algorithms. His primary research areas include robot path planning, swarm intelligence, and metaheuristic optimization. Fan’s most notable contribution is the development of a hybrid Tabu Particle Swarm Optimization algorithm integrated with Cauchy mutation, which addresses critical limitations in traditional PSO for robot path planning—namely slow convergence, premature local optima, and insufficient search capability in later stages. This innovative approach, published in 2024, has already garnered 2 citations, signaling early impact in the field. By combining the memory-based exploration of Tabu Search with the mutation-driven diversity of Cauchy distribution, Fan’s method significantly improves path planning efficiency and robustness. His work is particularly valuable for real-time robotic applications where computational speed and solution quality are paramount. Fan’s research bridges the gap between theoretical optimization and practical robotics, offering a promising direction for future autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1