In recent years, unmanned aerial vehicles (UAV) have been increasingly used in power line inspections. Birds often nest on transmission line towers, which threatens safe power line operation. The existing research on bird’s nest inspection using UAVs mainly stays at the level of image postprocessing detection, which has poor real-time performance and cannot obtain timely bird’s nest detection results. Considering the above shortcomings, we designed a power inspection UAV system based on deep learning technology for autonomous flight, positioning and photography, real-time bird nest detection, and result export. In this research, 2000 bird’s nest images in the actual power inspection environment were shot and collected to create the dataset. The parameter optimization and test comparison for bird’s nest detection are based on the three target detection models of YOLOv3, YOLOv5-s, and YOLOX-s. A YOLOv5-s bird’s nest detection model optimized for bird’s nest real-time detection is proposed, and it is deployed to the onboard computer for real-time detection and verification during flight. The DJI M300 RTK UAV was used to conduct a test flight in a natural power inspection environment. The test results show that the mAP of the UAV system designed in this paper for bird’s nest detection is 92.1%, and the real-time detection frame rate is 33.9 FPS. Compared with the previous research results, this paper proposes a new practice of using drones for bird’s nest detection, dramatically improving the real-time accuracy of bird’s nest detection. The UAV system can efficiently complete the task of bird’s nest detection in the process of electric power inspection, which can significantly reduce manpower consumption in the power inspection process.

Compared with traditional model-based fault detection and classification (FDC) methods, deep neural networks (DNN) prove to be more accurate for the aerospace sensors FDC problems. A concept known as imagefication-based intelligent FDC has been studied in recent years. This concept advocates to stack the sensors measurement data into an image format, the sensors FDC issue is transformed to abnormal regions detection problems on the stacked image. Although promising advances have been claimed, due to low size of the stacked image, small convolutional kernels and shallow DNN layers were used, the FDC performances were hindered. In this paper, we propose a data augmentation method which inflates the stacked image to a larger size (corresponding to the VGG16 object detection net in the machine vision realm). The FDC neural network is then trained via fine-tuning the VGG16. To compress the net size, layered channel pruning algorithm evaluates the importance of the convolutional filters enclosed in the net, channel pruning with knowledge distillation algorithm is then implemented, which yields a more lightweight FDC neural network. The fusion of Gradient-weighted Class Activation Mapping (Grad-CAM) and SHapley Additive exPlanations (SHAP) methods are adopted for explainability analysis of the FDC net, which clarifies its internal mathematical operations both locally (e.g. convolutions) and globally (e.g. deep Shaley values). Via data augmentation, fine-tuning from VGG16, and pruning, the FDC net developed in this paper claims an accuracy of 98.99% across 4 aircraft at 5 flight conditions. We also deploy the FDC net on real data from the flight of a model airplane, which claims an accuracy of 98.44%. Both high accuracy and explainability analysis presented in this paper justifies the efficacy of the proposed scheme.

Morphing aircraft are able to keep optimal performance in diverse flight conditions. However, the change in geometry always leads to challenges in the design of flight controllers. In this paper, a new method for designing a flight controller for variable-sweep morphing aircraft is presented—dynamic inversion combined with ${\mathcal{L}}_1$ adaptive control. Firstly, the dynamics of the vehicle is analyzed and a six degrees of freedom (6DOF) nonlinear dynamics model based on multibody dynamics theory is established. Secondly, nonlinear dynamic inversion (NDI) and incremental nonlinear dynamic inversion (INDI) are then employed to realize decoupling control. Thirdly, linear quadratic regulator (LQR) technique and ${\mathcal{L}}_1$ adaptive control are adopted to design the adaptive controller in order to improve robustness to uncertainties and ensure the control accuracy. Finally, extensive simulation experiments are performed, wherein the demonstrated results indicate that the proposed method overcomes the drawbacks of conventional methods and realizes an improvement in control performance.

This paper studies the cooperative control of multiple unmanned aerial vehicles (UAVs) with sensors and autonomous flight capabilities. In this paper, an architecture is proposed that takes a small quadrotor as a mission UAV and a large six-rotor as a platform UAV to provide an aerial take-off and landing platform and transport carrier for the mission UAV. The design of a tracking controller for an autonomous docking and landing trajectory system is the focus of this research. To examine the system’s overall design, a dual-machine trajectory-tracking control simulation platform is created via MATLAB/Simulink. Then, an autonomous docking and landing trajectory-tracking controller based on radial basis function proportional–integral–derivative control is designed, which fulfills the trajectory-tracking control requirements of the autonomous docking and landing process by efficiently suppressing the external airflow disturbance according to the simulation results. A YOLOv3-based vision pilot system is designed to calibrate the rate of the aerial docking and landing position to eight frames per second. The feasibility of the multi-rotor aerial autonomous docking and landing technology is verified using prototype flight tests during the day and at night. It lays a technical foundation for UAV transportation, autonomous take-off, landing in the air, and collaborative networking. In addition, compared with the existing technologies, our research completes the closed loop of the technical process through modeling, algorithm design and testing, virtual simulation verification, prototype manufacturing, and flight test, which have better realizability.

In recent years, a rising number of incidents between Unmanned Aerial Vehicles (UAVs) and planes have been reported at airports and airfields. A design scheme for an airport obstacle-free zone monitoring UAV system based on computer vision is proposed. The system integrates the functions of identification, tracking, and expelling and is mainly used for low-cost control of balloon airborne objects and small aircrafts. First, a quadcopter dynamic model and 2-Degrees of Freedom (2-DOF) Pan/Tilt/Zoom (PTZ) model are analyzed, and an attitude back-stepping controller based on disturbance compensation is designed. Second, a low and slow small-target self-identification and tracking technology is constructed against a complex environment. Based on the You Only Look Once (YOLO) and Kernel Correlation Filter (KCF) algorithms, an autonomous target recognition and high-speed tracking plan with great robustness and high reliability is designed. Third, a PTZ controller and automatic aiming strategy based on Anti-Windup Proportional Integral Derivative (PID) algorithm is designed, and a simplified, automatic-aiming expelling device, the environmentally friendly gel ball blaster, which features high speed and high accuracy, is built. The feasibility and stability of the project can be verified through prototype experiments.

A new fault detection scheme for aircraft Inertial Measurement Unit (IMU) sensors is developed in this paper. This scheme adopts a deep neural network with a CNN-LSTM-fusion architecture (CNN: convolution neural network; LSTM: long short-term memory). The fault detection network (FDN) developed in this paper is irrelative to aircraft model or flight condition. Flight data is reformed into a 2D format for FDN input and is mapped via the net to fault cases directly. We simulate different aircrafts with various flight conditions and separate them into training and testing sets. Part of the aircrafts and flight conditions appears only in the testing set to validate robustness and scalability of the FDN. Different architectures of FDN are studied, and an optimized architecture is obtained via ablation studies. An average detecting accuracy of 94.5% on 20 different cases is achieved.