On the 3D Track Planning for Electric Power Inspection Based on the Improved Ant Colony Optimization and Algorithm

Zheng Huang, Xuefeng Zhai, Hongxing Wang, Hang Zhou, Hongwei Zhao and Mingduan Feng

Mathematical Problems in Engineering, 2020, vol. 2020, 1-11

Abstract:

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.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:8295362

DOI: 10.1155/2020/8295362

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