Insulator faults detection is an important task for high-voltage transmission line inspection. However, current methods often suffer from the lack of accuracy and robustness. Moreover, these methods can only detect one fault in the insulator string, but cannot detect a multi-fault. In this paper, a novel method is proposed for insulator one fault and multi-fault detection in UAV-based aerial images, the backgrounds of which usually contain much complex interference. The shapes of the insulators also vary obviously due to the changes in filming angle and distance. To reduce the impact of complex interference on insulator faults detection, we make full use of the deep neural network to distinguish between insulators and background interference. First of all, plenty of insulator aerial images with manually labelled ground-truth are collected to construct a standard insulator detection dataset ‘InST_detection’. Secondly, a new convolutional network is proposed to obtain accurate insulator string positions in the aerial image. Finally, a novel fault detection method is proposed that can detect both insulator one fault and multi-fault in aerial images. Experimental results on a large number of aerial images show that our proposed method is more effective and efficient than the state-of-the-art insulator fault detection methods.

When a mobile robot inspects tasks with complex requirements indoors, the traditional backstepping method cannot guarantee the accuracy of the trajectory, leading to problems such as the instrument not being inside the image and focus failure when the robot grabs the image with high zoom. In order to solve this problem, this paper proposes an adaptive backstepping method based on double Q-learning for tracking and controlling the trajectory of mobile robots. We design the incremental model-free algorithm of Double-Q learning, which can quickly learn to rectify the trajectory tracking controller gain online. For the controller gain rectification problem in non-uniform state space exploration, we propose an incremental active learning exploration algorithm that incorporates memory playback as well as experience playback mechanisms to achieve online fast learning and controller gain rectification for agents. To verify the feasibility of the algorithm, we perform algorithm verification on different types of trajectories in Gazebo and physical platforms. The results show that the adaptive trajectory tracking control algorithm can be used to rectify the mobile robot trajectory tracking controller’s gain. Compared with the Backstepping-Fractional-Older PID controller and Fuzzy-Backstepping controller, Double Q-backstepping has better robustness, generalization, real-time, and stronger anti-disturbance capability.

Multicopters are well suited for executing fast maneuvers with high velocity, but they are still affected by the external atmospheric environment because attitude and position cannot be independently controlled. In this paper, we present a novel hexacopter which improves the wind resistance and strong coupling between attitude control and position control. The copter is designed such that the rotor sections can tilt around their respective arm axes. We present the aerodynamic methods to analyze the system dynamics model in windy environments. The entire system is decomposed into six loops based on the model, and the presented flight controller uses the ADRC method to consider both the attitude and the position. The controller introduces the extended state observer to estimate the white noise and wind disturbance. We use the nonlinear state error feedback law to control the output with disturbance compensation. Finally, we linearize the control allocation matrix that the controllers output directly mapped to the rotor velocities and tilting angles. The new theoretical results are thoroughly validated through comparative experiments.

This paper proposes a novel aerial manipulator with front cutting effector (AMFCE) to address the aerial physical interaction (APhI) problem. First, the system uncertainty and external disturbance during the system movement and contact operation are estimated by modeling the entire robot and contact position. Next, based on the established model, the nonlinear disturbance observer (NDO) is used to estimate and compensate the unknown external disturbance of the system and the uncertainty of the model parameters in real time. Then, the nonsingular terminal synovial membrane control method is used to suppress the part that is difficult to estimate. Finally, a controller which is suitable for the movement and operation of the entire system is designed. The controller’s performance is verified through experiments, and the results show that the design, modeling, and control of the entire system can achieve the APhI.

Aiming at the challenges of clearing tree obstacles along power transmission lines, the control and application of a novel Tree-Obstacle Clearing Coaxial Octocopter with Flexible Suspension Saw (TOCCO-FSS) have been investigated. Firstly, an overall scheme design and modeling of the TOCCO-FSS were conducted, and dynamic modeling of the TOCCO-FSS was performed using the Lagrange equation. Secondly, to address the interference encountered during the operation, a contact operation model was established to estimate the uncertainties and external disturbances during the contact operation process. Further, the Non-Singular Terminal Sliding-Mode Active Disturbance Rejection Control (NTSM-ADRC) method was researched based on the mathematical model of the TOCCO-FSS. Finally, the performance of the controller was verified through simulations and physical experiments. The results demonstrate that the design, control, and application of the entire TOCCO-FSS system are effective.

With the development of unmanned aerial vehicle (UAV) control technology, one of the recent trends in this research domain is to utilize UAVs to perform non-contact transmission line inspection. The RGB camera mounted on UAVs collects large numbers of images during the transmission line inspection, but most of them contain no critical components of transmission lines. Hence, it is a momentous task to adopt image classification algorithms to distinguish key images from all aerial images. In this work, we propose a novel classification method to remove redundant data and retain informative images. A novel transmission line scene dataset, namely TLS_dataset, is built to evaluate the classification performance of networks. Then, we propose a novel convolutional neural network (CNN), namely TL-Net, to classify transmission line scenes. In comparison to other typical deep learning networks, TL-Nets gain better classification accuracy and less memory consumption. The experimental results show that TL-Net101 gains 99.68% test accuracy on the TLS_dataset.