复杂环境中多无人机协同侦察的任务分配方法  

Task Allocation Method for Multi-UAV Cooperative Reconnaissance in Complex Environment

作　　者：张富震 朱耀琴[1] Zhang Fuzhen;Zhu Yaoqin(School of Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing 210094,China)

机构地区：[1]南京理工大学计算机科学与工程学院,江苏南京210094

出　　处：《系统仿真学报》2022年第10期2293-2302,共10页Journal of System Simulation

基　　金：省部级领域基金(2020-JCJQ-JJ-398);十四五装发预研课题(50904040201)。

摘　　要：现有多无人机协同规划方法往往将航迹规划与任务分配拆开单独解决,导致在复杂环境下协同方案并不是最佳。建立多无人机协同侦察异构目标的代价矩阵,针对复杂环境中多种障碍约束和无人机运动及航迹特点,以改进的PSO-AFSA (Particle Swarm Optimization-Artificial Fish Swarms Algorithm)求解单无人机航迹规划模型,利用匈牙利算法完成各架无人机侦察任务协同分配。仿真结果表明:该算法能使单无人机航程更短且航迹更光滑的同时,实现与任务协同分配紧耦合,使无人机集群总航程的全局总代价最低,提高了任务分配合理性。The existing cooperative planning methods of multiple UAVs often carry out path planning and task allocation separatly, which causes the cooperative scheme not being the best in a complex environment. The cost matrix of multi-UAV cooperative reconnaissance on heterogeneous targets is established. Aiming at the various obstacle constraints and the characteristics of UAV motion and track in complex environment, an improved PSO-AFSA is used to solve the single UAV track planning model.Hungarian algorithm is used to complete the cooperative allocation of reconnaissance tasks of UAVs.The simulation results show that the algorithm can make the flying range of single UAV shorter and the flight path smoother, and can realize the tight coupling with the task collaborative allocation, so and the total cost of the total flight range of UAV cluster is the lowest and the rationality of task allocation is improved.

关 键 词：粒子群算法 多机协同 异构目标 航迹规划 任务分配 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]