A multi-path planning method for unmanned aerial vehicle (UAV) in 3D environment

In order to solve the problems existing in the multi-path planning for UAV in the 3D environment, we improve the probability roadmap method (PRM) by moving the sampling points to increase their number in their narrow passage so that the PRM can better serve the multi-path planning environment. Then we propose the multi-objective ant colony algorithm (MACA) based on the PRM and apply it to the multi-path planning of the UAV. The MACA can optimize the path length and threat size of the UAV at the same time by updating their pheromones, thus obtaining a set of non-dominant solutions for the decision maker to select appropriate paths. To verify the effectiveness of the MACA, we simulate the multi-path planning environment as shown in Fig. 1; the simulation results, given in Fig.4 and 5 and Tables 1 and 2, and their analysis show preliminarily that our MACA based on PRM serves the 3D multi-path planning environment very well and can obtain a set of non-dominant solutions and converge to optimal solutions quickly.