At present, multirotor drones are restricted by the control accuracy and cannot position accurately according to the accuracy of point cloud data. Also, track planning in three-dimensional space is much more complicated than that in two-dimensional space, which means that existing track planning methods cannot achieve fast planning. Meanwhile, most existing researches were implemented in quasi-three-dimensional space with the shortest route length as the objective function and omitted environmental impacts. To overcome these, this paper uses the grid method to segment point cloud data of the flying space via ArcGIS software according to the drone’s controlling accuracy. It also extracts the grid coordinate information and maps it to a three-dimensional matrix to build the model accurately. This paper sets the minimal energy consumption as the objective function and builds a track planning model based on the drone’s performance and natural wind constraints. The improved ant colony optimization and (ACO-) algorithm are utilized to design this algorithm for a faster solution. That is, we use the improved ant colony optimization to quickly find a near-optimal track covering all viewpoints with the minimal energy consumption. The improved algorithm will be used for local planning for adjacent tracks passing through obstacles. In the designed simulation environment, the simulation results show that, to ensure that the same components are shot, the improved algorithm in this paper can save 62.88% energy compared to that of the Shooting Manual of Drone Inspection Images for Overhead Transmission Lines. Also, it can save 9.33% energy compared to a track with the shortest route length. Besides, the ACO- algorithm saves 96.6% time than the algorithm.

中文翻译：

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基于改进蚁群算法的电力检测3D跟踪规划

目前，多旋翼无人机受控制精度的限制，无法根据点云数据的精度进行精确定位。而且，三维空间中的轨道规划比二维空间中的轨道规划要复杂得多，这意味着现有的轨道规划方法无法实现快速规划。同时，大多数现有的研究都是在准三维空间中进行的，以最短路径长度为目标函数，并忽略了环境影响。为了克服这些问题，本文使用网格方法根据无人机的控制精度通过ArcGIS软件对飞行空间的点云数据进行了分割。它还提取网格坐标信息并将其映射到三维矩阵，以准确地构建模型。本文将最低能耗作为目标函数，并根据无人机的性能和自然风约束建立了航迹规划模型。改进的蚁群优化和（ACO- ）算法用于设计此算法以实现更快的解决方案。也就是说，我们使用改进的蚁群优化技术以最小的能耗快速找到覆盖所有视点的接近最佳的轨迹。改进的算法将用于通过障碍物的相邻轨道的局部规划。在设计的仿真环境中，仿真结果表明，为确保拍摄相同的组件，与《架空传输线无人机检查图像拍摄手册》相比，本文改进算法可节省62.88％的能量。而且，与最短路径的轨道相比，它可以节省9.33％的能量。此外，ACO算法比该算法节省了96.6％的时间。