电子测量与仪器学报

陈丽,陈洋,杨艳华.面向三维结构视觉检测的无人机覆盖路径规划[J].电子测量与仪器学报,2023,37(2):1-10

面向三维结构视觉检测的无人机覆盖路径规划

UAV coverage path planning for 3D structure visual inspection

DOI：

英文关键词:coverage path planning K-means clustering ant colony algorithm point cloud unmanned aerial vehicle

基金项目:国家自然科学基金(62173262, 61703314)项目资助

作者	单位
陈丽	1. 武汉科技大学机器人与智能系统研究院,2. 冶金自动化与检测技术教育部工程研究中心
陈洋	1. 武汉科技大学机器人与智能系统研究院,2. 冶金自动化与检测技术教育部工程研究中心
杨艳华	2. 冶金自动化与检测技术教育部工程研究中心

Author	Institution
Chen Li	1. Institute of Robotics and Intelligent Systems, Wuhan University of Science and Technology,2. Engineering Research Center of Metallurgical Automation and Detecting Technology of Ministry of Education
Chen Yang	1. Institute of Robotics and Intelligent Systems, Wuhan University of Science and Technology,2. Engineering Research Center of Metallurgical Automation and Detecting Technology of Ministry of Education
Yang Yanhua	2. Engineering Research Center of Metallurgical Automation and Detecting Technology of Ministry of Education

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中文摘要:

为了高效地规划无人机在三维覆盖检测任务的飞行路径,建立了满足覆盖率要求的路径规划模型,可分为两步:第 1 步确定无人机巡检的视点和视线,第 2 步计算视点的无碰撞访问序列。首先,从巡检目标的三维点云出发,提出基于 K 均值聚类生成候选视点的方法,构建候选视点互连的非完全图模型;其次,提出面向排序的混合蚁群算法( sorting-oriented hybrid ant colony algorithm,S-HACO)求取无人机巡检路径,优化目标考虑了路径长度、视点数目、急转弯次数等。仿真实验结果表明,该方法得到的视点数目较偏移法和随机采样法分别减少了 96. 25%和 42. 10%,并且 S-HACO 算法较传统算法相比性能更优,目标函数降低了 19. 14%,算法的运行时间减少了 25. 27%,验证了模型的有效性和算法的可行性。

英文摘要:

In order to efficiently plan the flight path of the UAV in the 3D coverage detection task, a path planning model that meets the coverage requirements is established, which can be divided into two steps: The first step is to determine the viewpoint and line of sight of the UAV inspection, and the second step calculate the collision-free access sequence of viewpoints. First, starting from the 3D point cloud of the inspection target, a method of generating candidate viewpoints based on K-means clustering is proposed, and an incomplete graph model of candidate viewpoint interconnections is constructed. Secondly, a sorting-oriented hybrid ant colony algorithm ( sortingoriented hybrid ant colony algorithm, S-HACO) finds the UAV inspection path, and the optimization goal takes into account the length of the path, the number of viewpoints, the number of sharp turns, etc. The simulation results show that the viewpoint obtained by this method compared with the offset method and random sampling method, the number is reduced by 96. 25% and 42. 10%, respectively, and the performance of the S-HACO algorithm is better than that of the traditional algorithm, the objective function is reduced by 19. 14%, and the running time of the algorithm is reduced by 25. 27%. The effectiveness of the model and the feasibility of the algorithm.

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