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.

During the inspection process of power pole towers, manual positioning is mainly used to select the shooting viewpoints of the drone, which leads to erroneous viewpoint selection and inaccurate shootings of inspected objects. Also, neglecting the effect of the suns backlight on photographs contributes to poor photo quality that does not meet inspection requirements. Aiming at the selection of shooting viewpoints during multirotor unmanned aerial vehicles inspection on power poles, this paper proposes an automatic positioning method that determines the shooting viewpoints by considering UAV performance, airborne camera parameters, and the size of objects to be measured. Considering the factors of sun illumination, we optimize the method to ensure the positions of the viewpoints and to ensure that the images can be clearly generated so that the observers can check the power pole towers through the images when shooting is also taken into consideration. Finally, the automatic calculation method of the related viewpoints is implemented in the Java language. Experiments show that the method can accurately obtain the positions of the drones viewpoints and reduce the number of viewpoints, which significantly improves the efficiency and quality of inspection shooting.

Recently, more and more unmanned aerial vehicles (UAVs) have been used in industry, public services and commercial fields. However, the UAV is generally limited by the battery capacity, and the operation radius is greatly limited. In this paper, to increase the flight time of the UAV, the recharging station by using wireless power transfer (WPT) technology is proposed. A new receiver magnetic coupler is given, the receiver coil is wound around the hollow ferrite core, and the proposed structure provides many benefits: 1) first, there is a large magnetic flux captured surface, which can improve the mutual coupling between the primary side and secondary side. 2) Second, own to the hollow ferrite core, the receiver side is light, which has little effect on the weight bearing of UAV. 3) Third, it is easy to be installed on the UAV, since the receiving coil is compact. 4) Moreover, the proposed WPT system provides enough anti misalignment capability for UAV.