UAV Path Planning Based on Ant Colony Algorithm
Luoyang Luo · 2023
This paper proposes a novel approach for unmanned aerial vehicle (UAV) flight path planning using ant colony optimization (ACO). The method compares the target location to a food source pursued by ants, dividing the enemy defense zone into equidistant grids. Ants traverse different grid points to reach the target location. The path selection of ants is determined using the visibility function of the ACO algorithm, considering enemy threats and distance to the target node. Additionally, a scoring function is introduced, considering flight path length, threat cost, and maximum deviation angle constraints. Pheromone concentration on the path is updated based on the scoring function. The optimal UAV flight path is represented as a set of node numbers received by the ants during the search. The ACO-based UAV flight path planning method is simple, easy to implement, and exhibits significant optimization effects. Through simulation experiments, we demonstrate the effectiveness and superiority of the method.