Path planning of uninhabited combat air vehicle based on Voronoi diagram and ant colony optimization algorithm

Path planning of Uninhabited Combat Air Vehicle (UCAV) is a complicated optimum problem, and a common graphical technique for optimal path planning against multiple threat sources is to make use of the Voronoi diagram. Ant Colony Optimization (ACO) algorithm is a heuristic bionic algorithm for the approximate solution of combinatorial optimization problems, which has been inspired by the foraging behavior of real ant colonies. Firstly, the weighted Voronoi diagram was created according to the certain threat sources, and the total cost of each edge could be calculated according to the threats cost and the fuel cost. Then, the improved ACO mathematical model for UCAV path planning was proposed. Finally, a hybrid Voronoi diagram and ACO approach to UCAV path planning was put forward. Series simulation results demonstrate the proposed hybrid method is feasible and effective in UCAV path planning under various combat field environments.