Efficient path planning for UAV formation via comprehensively improved particle swarm optimization
Efficient path planning for UAV formation via comprehensively improved particle swarm optimization
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DOI：
10.1016/j.isatra.2019.08.018
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发表时间：
2020-02-01
期刊：
影响因子：
7.3
通讯作者：
Du, Yun
中科院分区：
文献类型：
--
作者：
Shao, Shikai;Peng, Yu;Du, Yun
自动生成最优飞行路径是无人机编队系统的关键技术和挑战。为提高无人机编队航迹规划的快速性和最优性，提出了一种基于综合改进粒子群算法的无人机编队三维航迹规划算法。在该方法中，首先采用基于混沌的Logistic映射，以改善粒子的初始分布。然后，将常用的常加速度系数和最大速度系数设计为自适应线性变化系数，以适应优化过程，同时提高解的最优性。此外，还提出了用期望粒子替换非期望粒子的变异策略，加快了算法的收敛速度。理论上，综合改进后的粒子群算法不仅加快了收敛速度，而且提高了解的最优性。最后，对地形和威胁约束下的无人机编队进行了蒙特-卡罗仿真，仿真结果表明了该方法的快速性和最优性。(C)2019年伊萨。由爱思唯尔有限公司出版。保留所有权利。
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. (C) 2019 ISA. Published by Elsevier Ltd. All rights reserved.