多任务执行中无人机行动联盟形成模型及算法

针对多无人机(unmanned aerial vehicle, UAV)多任务执行问题,开展基于行动联盟的任务执行策略研究。分析了无人机行动联盟形成 (UAV action coalition formation, UACF) 策略的约束条件,建立了以最小化任务完成时间为目标函数的数学模型;设计了求解该模型的分阶段贪心规划算法 (phased greedy planning algorithm, PGPA),在进行算法状态空间描述的基础上,给出了包括任务选取、无人机〖CD*2〗任务匹配和资源分发策略等在内的算法流程;最后,通过多组仿真实验,验证了算法的有效性和优越性。

UAV action coalition formation model and algorithm in multi-task execution

To solve the multi-unmanned aerial vehicle (UAV) executing multi-task problem, this paper carries out the task execution strategy based on action coalition. Firstly, it analyzes constraint conditions in the UAV action coalition formation (UACF) strategy, and it establishes the mathematical model which takes task completion time as the objective function. After that, a novel algorithm solving the model called phased greedy planning algorithm (PGPA) is designed, and on the basis of describing the state space of the algorithm, the detailed flow, including task selection, UAV-task matching and resource distribution strategy of the algorithm, are offered. Finally, the effectiveness and superiority of the algorithm are verified through multiple sets of simulation experiments.