UAV path planning method based on ant colony optimization

Abstract

A new UAV path planning method based on ant colony optimization (ACO) is presented. The target position is considered as the food source which the ants are going to find. The enemy defense region is considered as the searching area of the ants and is divided into equally spaced grids. The ants move to the destination node through several nodes on the grid region. The visibility function of ACO algorithm considers the enemy threats intensity on the paths and the distance to the destination node. The weighted sums of the flight path length, the threat cost and the maximum restriction of the yaw angle are considered as the evaluation function of ACO algorithm. The pheromone amounts on the paths are updated according to the evaluation function values. Therefore, the UAV optimal flight path is expressed by a group of node number, which is obtained by the ants finding the optimal route to the food source. The ACO algorithm based UAV path planning method is characterized as simple coding and good optimization guidance, and the simulation results also show its effectiveness.