Path Planning of UAV for Oilfield Inspection Based on Improved Grey Wolf Optimization Algorithm

Fawei Ge, Kun Li, Wensu Xu, Yi'an Wang
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引用次数: 19

Abstract

It is difficult for the traditional manual inspection method to satisfy the current management requirements. Now, UAV inspection technology has been used by more and more enterprises. In the UAV inspection, path planning is an important work. An improved grey wolf optimization algorithm is proposed for the path planning of UAV in oilfield environment in this paper. Firstly, the model of the oilfield environment is built; secondly, the initial path is produced by the basic grey wolf optimization (GWO) algorithm; and then, the fruit fly optimization (FOA) algorithm is used to continue the local optimization of the optimal solution; finally, the optimal path is generated. Compared with some other methods, the simulation results show that the improved grey wolf optimization algorithm is effective.
基于改进灰狼优化算法的无人机油田巡检路径规划
传统的人工检测方法已难以满足当前的管理要求。目前,无人机巡检技术已经被越来越多的企业所采用。在无人机巡检中,路径规划是一项重要的工作。针对油田环境下无人机的路径规划问题,提出了一种改进的灰狼优化算法。首先,建立了油田环境模型;其次,利用基本灰狼优化算法生成初始路径;然后,采用果蝇优化(FOA)算法继续对最优解进行局部优化;最后,生成最优路径。仿真结果表明,改进的灰狼优化算法是有效的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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