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Distribution Planning of UAV Automatic Charging Station Based on Genetic Algorithm

机译:基于遗传算法的无人机自动充电站布局规划

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As the UAV industry develops rapidly, UAVs are seeing wider application. They have the potential to play a crucial role in future smart cities and bring in enormous economic benefits. Presently, the battery life of UAVs is short and it is difficult to make breakthroughs in the short term. In addition, the requirements for UAV battery life get higher and higher. Under this situation, to deploy autonomous charging stations for UAVs becomes an inevitable trend. However, it remains a challenge to minimize the deployment cost due to various factors involved. Against the backdrop of taking the lead in putting forward the planning and layout of UAV charging station, this paper carried out the cost modeling of various factors affecting the cost in the charging process of the initial station and the later UAVs. Moreover, this paper used the weighted Voronoi diagram to introduce the influence of real environmental factors on the layout planning of charging station, flight route and working efficiency, thus quantifying the cost of the space environment. Finally, the comprehensive cost model was established and the optimal location of UAV charging station was selected by iterative optimization calculation based on genetic algorithm. Additionally, the paper selected the practical examples, assigned the parameters reasonably, solved the self-built model using the genetic algorithm, adjusted the iteration times and the corresponding parameters of the algorithm and obtained the optimization results finally. Therefore, applicability of the model as well as feasibility and optimality of the algorithm were verified. Under the condition of modifying and optimizing the parameters and corresponding values of the model in this paper, the calculation process of genetic algorithm was adjusted appropriately. The models and methods in this paper could be applied to the planning of UAV charging stations in the future under the actual comprehensive situation of cities or other regions, which maximizes some of the economic benefits in the field of UAV applications and the coincidence with the actual situation.
机译:随着无人机产业的快速发展,无人机得到了越来越广泛的应用。他们有潜力在未来的智慧城市中发挥关键作用,并带来巨大的经济利益。目前,无人机的电池寿命短,短期内难以突破。另外,对无人机电池寿命的要求也越来越高。在这种情况下,为无人机部署自主充电站已成为必然趋势。然而,由于涉及各种因素,使部署成本最小化仍然是一个挑战。在带头提出无人机充电站的规划和布局的背景下,本文对影响初始充电站和后续无人机充电过程中成本的各种因素进行了成本建模。此外,本文利用加权Voronoi图介绍了实际环境因素对充电站布局规划,飞行路线和工作效率的影响,从而量化了空间环境的成本。最后,建立了综合成本模型,通过基于遗传算法的迭代优化计算,选择了无人机充电站的最优位置。此外,本文还选择了实例,合理分配参数,使用遗传算法求解自建模型,调整迭代次数和算法的相应参数,最终得到优化结果。因此,验证了模型的适用性以及算法的可行性和最优性。在修改和优化模型参数及相应值的条件下,适当调整了遗传算法的计算过程。本文的模型和方法可以在城市或其他地区的实际综合情况下,应用于未来的无人机充电站规划中,从而最大化了无人机应用领域的一些经济效益,并与实际相吻合。情况。

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