Based on PSO algorithm multiple task assignments for cooperating UAVs

Wang Xinzeng, Ci Linlin, Lin Junshan, Yu Ning
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引用次数: 5

Abstract

This paper presents a novel approach to multiple task assignments for cooperating unmanned aerial vehicles (UAVs).Aim at the problem of assigning cooperating UAVs to perform multiple task that includes identifying and attack, along with subsequent battle damage verification on multiple targets, the multiple task assignments model is built, It not only takes into account the requirements of the scenario such as task precedence and coordination, timing constraints, trajectories limitations, but also thinks the requirements of task for weapon kinds of UAV and capability of UAV and flyable trajectories, etc. The optimal solution of the multiple task assignments is an NP-hard problem. When some of the tasks must be accomplished at specified time and with specific vehicles and specific weapon, the problem becomes highly complex, and search for an optimum solution may be a very difficult task. The particle swarm optimization algorithm is improved and is used for solving multiple task assignments problem. We construct a double layer position vector spaces, it makes the particle of the particle swarm optimization algorithm correspond to the feasible solution of multiple task assignments problem. Simulations showing that it consistently and quickly provides good feasible solutions.
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基于粒子群算法的协作无人机多任务分配
提出了一种协作型无人机多任务分配的新方法。针对协同无人机对多个目标进行识别、攻击及后续战损验证的多任务分配问题,建立了多任务分配模型,该模型不仅考虑了任务优先级与协调性、时间约束、轨迹限制等场景要求,还考虑了任务对无人机武器种类、无人机能力和可飞轨迹等方面的要求。多任务分配的最优解是一个np困难问题。当某些任务必须在特定的时间、特定的车辆和特定的武器完成时,问题就变得非常复杂,寻找最优解可能是一项非常困难的任务。改进了粒子群优化算法,并将其用于解决多任务分配问题。构造了双层位置向量空间,使粒子群优化算法中的粒子对应于多任务分配问题的可行解。仿真结果表明,该方法能够快速、一致地提供较好的可行解。
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