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以多异构无人机执行SEAD任务为背景,开展协同任务分配问题建模、算法设计和仿真分析.采用图论的方法完成问题的建模,将无人机本体等效为Dubins Car模型,并对其在相应目标处执行侦查、打击、评估任务时的进入角度进行约束,通过Dubins路径完成对无人机飞行路径的等效,采用分布式遗传算法完成对问题的快速求解.研究结果表明,带有路径末端角度约束的任务分配问题具有较好的实用意义,分布式遗传算法可有效处理实时任务分配问题,完成任务空间的快速决策.
Based on the multi-heterogeneous UAV implementation of SEAD mission, this dissertation carries out modeling, algorithm design and simulation analysis of collaborative task assignment.Using graph theory to solve the problem modeling, the UAV is equivalent to Dubins Car model, And constrains the angle of entry of the UAV during its detection, combat and evaluation tasks at the corresponding target, completes the flight path equivalent to the UAV through the Dubins path, and uses a distributed genetic algorithm to solve the problem quickly. The results show that , The problem of task assignment with angle constraints at the end of the path has good practical significance. Distributed genetic algorithm can effectively deal with the problem of real-time task assignment and complete the rapid decision-making in task space.