Optimization of Pesticide Spraying Tasks via Multi-UAVs Using Genetic Algorithm
Optimization of Pesticide Spraying Tasks via Multi-UAVs Using Genetic Algorithm
复制标题
利用遗传算法优化多无人机农药喷洒任务
DOI：
10.1155/2017/7139157
复制
发表时间：
2017
影响因子：
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通讯作者：
Ma Huawei
中科院分区：
文献类型：
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作者：
Luo He;Niu Yanqiu;Zhu Moning;Hu Xiaoxuan;Ma Huawei
任务分配是无人机喷洒农药过程中的关键问题，使喷洒效果最大化是优化无人机喷洒农药的目标。在本研究中，我们首先引入每个无人机的运动学约束，并将场间欧氏距离扩展到杜宾路径距离。然后分析了影响农药喷洒效果的两个因素，即农药种类和喷洒时的温度。根据温度动态生成农药喷洒的时间窗口，并将其引入农药喷洒功效函数。最后，在此基础上，提出了一个具有变时间窗和变利润模型的团队定向问题。我们提出了一种遗传算法来解决上述模型，并给出了算法的编码、交叉和变异方法。实验结果表明，该模型及其求解方法相对于常用的人工分配策略具有明显的优势，并且在小规模场景下可以提供与枚举方法相同的结果。此外，结果还表明算法参数会影响解，并为算法提供了最优参数配置。
Task allocation is the key factor in the spraying pesticides process using unmanned aerial vehicles (UAVs), and maximizing the effects of pesticide spraying is the goal of optimizing UAV pesticide spraying. In this study, we first introduce each UAV’s kinematic constraint and extend the Euclidean distance between fields to the Dubins path distance. We then analyze the two factors affecting the pesticide spraying effects, which are the type of pesticides and the temperature during the pesticide spraying. The time window of the pesticide spraying is dynamically generated according to the temperature and is introduced to the pesticide spraying efficacy function. Finally, according to the extensions, we propose a team orienteering problem with variable time windows and variable profits model. We propose the genetic algorithm to solve the above model and give the methods of encoding, crossover, and mutation in the algorithm. The experimental results show that this model and its solution method have clear advantages over the common manual allocation strategy and can provide the same results as those of the enumeration method in small‐scale scenarios. In addition, the results also show that the algorithm parameter can affect the solution, and we provide the optimal parameters configuration for the algorithm.