Efficient path planning for UAV formation via comprehensively improved particle swarm optimization

Abstract

Automatic generation of optimized flyable path is a key technology and challenge for autonomous unmanned aerial vehicle (UAV) formation system. Aiming to improve the rapidity and optimality of automatic path planner, this paper presents a three dimensional path planning algorithm for UAV formation based on comprehensively improved particle swarm optimization (PSO). In the proposed method, a chaos-based Logistic map is firstly adopted to improve the particle initial distribution. Then, the common used constant acceleration coefficients and maximum velocity are designed to adaptive linear-varying ones, which adjusts to the optimization process and meanwhile improves solution optimality. Besides, a mutation strategy that undesired particles are replaced by those desired ones is also proposed and the algorithm convergence speed is accelerated. Theoretically, the comprehensively improved PSO not only speeds up the convergence but also improves the solution optimality. Finally, Monte-Carlo simulation for UAV formation under terrain and threat constraints are carried out and the results illustrate the rapidity and optimality of the proposed method.