基于动态簇粒子群优化的无人机集群路径规划方法  

Route planning method of UAV swarm based on dynamic cluster particle swarm optimization

作　　者：王龙宝[1,2] 栾茵琪 徐亮 曾昕 张帅 徐淑芳[1,2] WANG Longbao;LUAN Yinqi;XU Liang;ZENG Xin;ZHANG Shuai;XU Shufang(College of Computer and Information,Hohai University,Nanjing Jiangsu 211100,China;Key Laboratory of Water Big Data Technology of Ministry of Water Resources(Hohai University),Nanjing Jiangsu 211100,China;Yangtze Ecology and Environment Company Limited,Wuhan Hubei 430014,China;Power China Kunming Engineering Corporation Limited,Kunming Yunnan 650051,China)

机构地区：[1]河海大学计算机与信息学院,南京211000 [2]水利部水利大数据技术重点实验室(河海大学),南京211000 [3]长江生态环保集团有限公司,武汉430014 [4]中国电建集团昆明勘测设计研究院有限公司,昆明650051

出　　处：《计算机应用》2023年第12期3816-3823,共8页journal of Computer Applications

基　　金：云南省科技厅重大科技专项计划项目(202202AF080003);长江生态环保集团有限公司科研项目(HBZB2022005)。

摘　　要：路径规划对于无人机(UAV)集群的任务执行十分重要,而且高维场景中的计算通常很复杂。群体智能为解决该问题提供了较好的解决思路。粒子群优化(PSO)算法具有参数少、收敛速度快、操作简单等优点,尤其适用于路径规划问题,但它在应用时存在全局搜索能力差、容易陷入局部最优的问题。为了解决上述问题以提升无人机集群路径规划的效果,提出了动态簇粒子群优化(DCPSO)算法。首先,利用人工势场法和滚动时域控制原理建模UAV集群路径规划问题的任务场景;其次,引入Tent混沌映射和动态簇机制进一步提升全局搜索能力和搜索精度;最后,使用DCPSO算法优化模型的目标函数,以获得UAV集群的每个轨迹点的选择。在单峰/多峰、低维/高维不同组合的10种基准测试函数下的仿真实验结果表明,与PSO、鸽子启发优化(PIO)、麻雀搜索算法(SSA)和混沌扰动鸽群优化(CDPIO)算法相比,DCPSO算法具有更好的计算最优值、均值和方差,搜索精度更佳,稳定性更强。此外,UAV集群路径规划应用实例仿真结果也验证了DCPSO算法的性能与效果。Route planning is very important for the task execution of Unmanned Aerial Vehicle(UAV)swarm,and the computation is usually complex in high dimensional scenarios.Swarm intelligence has provided a good solution for this problem.Particle Swarm Optimization(PSO)algorithm is especially suitable for route planning problem because of its advantages such as few parameters,fast convergence and simple operation.However,PSO algorithm has poor global search ability and is easy to fall into local optimum when applied to route planning.In order to solve the problems above and improve the effect of UAV swarm route planning,a Dynamic Cluster Particle Swarm Optimization(DCPSO)algorithm was proposed.Firstly,artificial potential field method and receding horizon control principle were used to model the task scenario of route planning problem of UAV swarm.Secondly,Tent chaotic map and dynamic cluster mechanism were introduced to further improve the global search ability and search accuracy.Finally,DCPSO algorithm was used to optimize the objective function of the model to obtain each trajectory point selection of UAV swarm.On 10 benchmark functions with different combinations of unimodal/multimodal and low-dimension/high-dimension,simulation experiments were carried out.The results show that compared with PSO algorithm,Pigeon-Inspired Optimization(PIO),Sparrow Search Algorithm(SSA)and Chaotic Disturbance Pigeon-Inspired Optimization(CDPIO)algorithm,DCPSO algorithm has better optimal value,mean value and variance,better search accuracy and stronger stability.Besides,the performance and effect of DCPSO algorithm were demonstrated in the route planning application instances of UAV swarm simulation experiments.

关 键 词：粒子群优化 动态簇机制 无人机集群 路径规划 滚动时域控制 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]