关键词: 路径规划, 蚁群算法, 无人机, 信息素

Abstract: Aiming at the problems of slow planning speed and easy to fall into local optimization when the traditional ant colony algorithm is used for path planning of UAV 3D environment, this paper proposes three improved strategies:changing the state transition rules with the guidance function, the prior distribution of the initial pheromone, and the time-varying pheromone update method, fully mining the prior information of path planning, enhancing the path by adding the guidance function, and increasing the probability of selecting the optimal path. At the same time, the pheromone is given a different initial concentration according to the distance from the prior path, so that the algorithm has a clear direction in the initial search. The pheromone is updated based on the idea of survival of the fittest, and the pheromone volatilization factor is set as a fluctuation factor that obeys the Laplace distribution, so as to avoid the search process from falling into local optimization, maximize the path search efficiency, and realize the path planning of UAV in the 3D environment. The simulation results show that the improved ant colony algorithm is superior to the traditional ant colony algorithm in planning the optimal path length and searching efficiency.

Key words: path planning, ant colony algorithm, UAV, pheromone