基于遗传算法的无人水面车辆协同任务分配

Qinghua Luo, Xiaozhen Yan, Di Wu, Ruochen Ding
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引用次数: 0

摘要

任务分配建模在无人水面车辆协同系统中起着重要的作用。为了适应复杂环境,设计了一种适用于多usv、多目标、多任务的协同多任务分配问题(CMTAP)模型。本文首先阐明了协作的优势,然后在传统遗传算法(GA)的基础上,对交叉和变异算子进行了优化,使其更适合当前环境。该方法利用遗传算法强大的全局搜索能力对USV的协同任务分配结果进行优化。仿真实验证明了该方法的有效性。
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Unmanned Surface Vehicle Cooperative Task Assignment Based on Genetic Algorithm
Task allocation modeling plays an important role in unmanned surface vehicles (USV) collaborative systems. In order to adapt to the complex environment, a cooperative multi-task assignment problem (CMTAP) model suitable for multi-USV, multi-target, and multi-task is designed. The article first clarifies the advantages of collaboration, then based on the traditional genetic algorithm (GA), the crossover and mutation operators are optimized to be more suitable for the current environment. This method utilizes the strong global search ability of GA to optimize the result of cooperative task assignment of USV. Simulation experiments demonstrate the effectiveness of the method.
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