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

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 A∗ (ACO-A∗) 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 A∗ 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-A∗ algorithm saves 96.6% time than the A∗ algorithm.

Research on Multi-AGVs Path Planning and Coordination Mechanism

“…The pheromone concentration between adjacent nodes and the expected heuristic information between adjacent nodes calculated by the heuristic function are used to search path, and the transition probability function ( ) can be described as follows [29]:…”

The Collaborative Power Inspection Task Allocation Method of “Unmanned Aerial Vehicle and Operating Vehicle”

“…The integration of OV and UAV combines the advantages of both sides and enhances the professionalism and efficiency of power inspection work. At present, the inspection process, OV path, and drone task allocation process still rely on the experience of inspectors [8] . Operating vehicles and drones' scheduling schemes directly affect the efficiency of the power inspection.…”