基于多目标优化算法的异构多无人机协同任务分配（英文）

Cooperative task allocation for heterogeneous multi-UAV using multi-objective optimization algorithm

近年来,关于多无人机在复杂任务中的应用有了广泛的探索。无人机具有部署灵活、成本低廉和自持力强的优点,合理的协同方案可以实现无人机之间的高效信息融合与资源互补,突破单架无人机能力限制,提升任务执行效率。在现实应用中,异构任务分配需要满足多种现实约束,如飞行平台差异、任务时间窗、任务耦合关系等。因此,作为无人机集群应用的顶层规划,异构无人机任务分配可以建模为基于无人机动力学约束的多目标优化问题。在问题模型构建中,设计了多层编码策略和约束调度方法来处理多种耦合约束,引入了任务时间、任务收益、无人机损耗和飞行航程四个优化目标对分配方案进行综合评估。为高效求解这一高维多目标优化问题,本文提出了一种改进的多目标量子粒子群算法,即利用改进的非支配解评估方法来引导算法进化,综合考虑解的收敛性和分布性,并保留了合理的边界解。仿真结果验证了构建模型的可行性与改进算法的有效性。

The application of multiple UAVs in complicated tasks has been widely explored in recent years.Due to the advantages of flexibility,cheapness and consistence,the performance of heterogeneous multi-UAVs with proper cooperative task allocation is superior to over the single UAV.Accordingly,several constraints should be satisfied to realize the efficient cooperation,such as special time-window,variant equipment,specified execution sequence.Hence,a proper task allocation in UAVs is the crucial point for the final success.The task allocation problem of the heterogeneous UAVs can be formulated as a multi-objective optimization problem coupled with the UAV dynamics.To this end,a multi-layer encoding strategy and a constraint scheduling method are designed to handle the critical logical and physical constraints.In addition,four optimization objectives:completion time,target reward,UAV damage,and total range,are introduced to evaluate various allocation plans.Subsequently,to efficiently solve the multi-objective optimization problem,an improved multi-objective quantum-behaved particle swarm optimization(IMOQPSO)algorithm is proposed.During this algorithm,a modified solution evaluation method is designed to guide algorithmic evolution;both the convergence and distribution of particles are considered comprehensively;and boundary solutions which may produce some special allocation plans are preserved.Moreover,adaptive parameter control and mixed update mechanism are also introduced in this algorithm.Finally,both the proposed model and algorithm are verified by simulation experiments.