Shao Shikai, Peng Yu, He Chenglong, Du Yun
School of Electrical Engineering, Hebei University of Science and Technology, Shijiazhuang, 050018, China.
School of Electrical Engineering, Hebei University of Science and Technology, Shijiazhuang, 050018, China.
ISA Trans. 2020 Feb;97:415-430. doi: 10.1016/j.isatra.2019.08.018. Epub 2019 Aug 8.
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.
自动生成优化的可飞行路径是自主无人机编队系统的一项关键技术和挑战。为了提高自动路径规划器的快速性和最优性，本文提出了一种基于综合改进粒子群优化（PSO）的无人机编队三维路径规划算法。在所提方法中，首先采用基于混沌的Logistic映射来改善粒子初始分布。然后，将常用的恒定加速度系数和最大速度设计为自适应线性变化的系数和速度，使其适应优化过程，同时提高解的最优性。此外，还提出了一种将不良粒子替换为优良粒子的变异策略，加速了算法收敛速度。从理论上讲，综合改进的粒子群优化算法不仅加快了收敛速度，还提高了解的最优性。最后，进行了地形和威胁约束下无人机编队的蒙特卡洛仿真，结果验证了所提方法的快速性和最优性。
