UAV Route Planning Based on Fuzzy PID Control Ant Colony Algorithm
Haolin Yang, Jianyong Zheng, Yao Li, Zhichao Zhang · 2023
This paper proposes an improved ant colony algorithm combining fuzzy PID control and adaptive weight information adjustment to optimize the Unmanned Aerial Vehicle(UAV) route planning technology. Most of the existing UAV route planning still stays on a single algorithm, and the problems such as low iterative efficiency and poor robustness for route planning cannot be effectively solved. Therefore, this paper first improved the pheromone update strategy of common ant colony algorithm to further improve the pheromone update effect. Secondly, the paper introduced fuzzy control and convergence state pheromone increment to promote the global optimal convergence of the algorithm. Finally, the paper employed adaptive PID weight factor adjustment to expedite the ant colony algorithm's rapid iteration and convergence, thereby enhancing the global convergence route optimization results. Compared to the ant colony algorithm, the algorithm proposed in this paper exhibits an approximately 20% increase in the efficiency of route planning.