Mission planning for multiple UAVs based on ant colony optimization and improved Dubins path

Abstract

This paper presents a mission planning method for multiple unmanned aircraft vehicles (UAVs) attacking multiple ground targets. The mission planning method includes mission allocation based on ant colony optimization (ACO) and path planning based on improved Dubins path. First, the multiple targets attacking problem is transformed to an optimization problem. Second, to make the method more applicative for real battlefield, several common constraints are considered in the optimization problem. The constraints of mission allocation mainly include the UAVs' executive ability, flight range, targets' threat degree and attack profit, etc. The constraints of path planning mainly include the minimum turning radius and length of path. To meet the goal of optimization of multiple performance indexes, comprehensive performance index is designed. Moreover, the Dubins path is adopted to generate flight paths for every UAV. And some improvement of Dubins path is made to fit the path planning problem. The improved Dubins path is easier to be calculated and shorter than the original Dubins path. Finally, an example of multiple UAVs attacking multiple ground targets is carried out to verify the feasibility of strategies for mission planning. Results of MATLAB simulations indicate that the strategy is feasible to the problems of mission planning.