Dynamic Path Planning of UAV Based on Pheromone Diffusion Ant Colony Algorithm

Due to the dynamic uncertainty factors in a complex environment, such as flight conditions, movable obstacles and other sudden threats. It is a challenge to realize the real-time path planning of Unmanned Aerial Vehicles (UAV). In this paper, the method is proposed with a model of the dynamic environment and a method of pheromone diffusion ant colony optimization (PDACO) to solve the real-time path planning of UAV in a dynamic environment. The translational obstacle method and the random obstacle method can efficiently simulate the dynamic environment. PDACO takes advantage of pheromone diffusion characteristics in an ant colony, and diffuses the pheromones to adjacent paths after each iteration, thus expanding the guidance range of pheromones. When the environment changes, the pheromone diffusion method can quickly plan new paths and accelerate the convergence of the algorithm. Simulation results show that the dynamic environment model accords with the actual situation. Compared with four algorithms, PDACO ensures that the UAV can optimize a new path with shorter path length and computing time when environment changes. The proposed method is feasible and effective.