Abstract:

To deal with dynamic routes planning of unmanned aerial vehicles in a complicated environment, a new method that combines ant colony optimization with artificial potential is proposed. The mission region is described as a grid model. In the route search process, ants are influenced not only by pheromone and heuristic information, but also by the potential field force. According to the node location’s potential field, the state transition rules consist of deterministic choice and probabilistic choice. The environmental perception factor is designed for dynamically adjusting the proportion of deterministic choice. In order to make full use of the known environmental information and guide the ant’s search, the potential field direction and the distance between the candidate node and the goal are used to construct comprehensive heuristic information. Simulation results show that the proposed method can effectively obtain optimal feasible routes. The optimization result is better than that of the simplex ant colony and artificial field, and has better convergence speed and optimization precision.

Key words: route planning, ant colony optimization, artificial potential, unmanned aerial vehicle