基于蚁群算法的无人机协同多任务分配

采用蚁群算法对无人机协同多任务分配问题(CMTAP)进行研究。在通用CMTAP模型的基础上,综合考虑包括动态任务时间约束和无人机任务能力的差别多类复杂约束条件,建立扩展的协同多任务分配模型。在多子群蚁群算法的基础上,提出了基于分工机制的蚁群算法对CMTAP进行求解。根据协同多任务分配的特点,设计了基于任务能力评估的问题解构造策略和基于任务代价的状态转移规则,提高了算法的性能。仿真实验结果表明该方法能有效地解决无人机协同多任务分配问题。

UAV Cooperative Multi-task Assignment Based on Ant Colony Algorithm

Ant colony algorithm is applied to solve unmanned aerial vehicle(UAV) cooperative multi-task assign-ment problem(CMTAP).Based on the generic formulation of CMTAP,an extended mathematical formulation for the UAV CMTAP is presented,which takes the dynamic time window constraints and the capabilities of UAVs into ac-count.Then,a multi-ant-colony algorithm based on the job-division mechanism is put forward to solve the problem.According to the characteristics of CMTAP,the strategy for construction of problem solution based on mission capa-bilities of UAV and state transition rules based on mission cost are designed to generate the feasible solutions of CMTAP and improve the performance of algorithm.The simulation results demonstrate the feasibility and efficiency of the algorithm put forward.