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针对多无人机应用于城市环境问题,设计了一种MUAV与SUAV层次化任务分配方案,并分析了MUAV对SUAV执行目标任务成功率的影响,将影响因子加入目标函数,提出了一种无人机探测范围内的层次化任务分配模型.采用连续粒子群(PSO)算法对问题进行求解,通过加入惯性权重的凹函数递减策略与将人工蜂群(ABC)算法引入到粒子群迭代环节,较好地解决粒子群算法易陷入局部最优的问题,同时提高算法收敛速度.仿真结果表明所提出的模型可以较好地解决城市环境下的多无人机层次化任务分配问题.
Aiming at the multi-UAV applied to the urban environment, a hierarchical task assignment scheme based on MUAV and SUAV is designed and the influence of MUAV on the success rate of SUAV’s execution of target task is analyzed. An impact factor is added to the objective function, The hierarchical task assignment model in man-machine exploration scope is solved by using continuous particle swarm optimization (PSO) algorithm. By adding the concave function decreasing strategy of inertia weight and introducing the artificial bee colony (ABC) algorithm into the particle swarm iteration, Which solves the problem of Particle Swarm Optimization (PSO), which is easy to fall into the local optimum and improves the convergence speed of the algorithm.The simulation results show that the proposed model can better solve the multi-UAV hierarchical task assignment problem in urban environment.