In the field of aerial photography, UAVs can be used as an ideal carrier of sensors. It is meaningful to plan its coverage path combined with the sensor model. Previous works mainly used decentralized coverage to realize UAV formation cooperative operation, but for the aerial photography scene of a single task, the decentralized coverage algorithm is not effective. In this paper, a centralized coverage algorithm of UAV formation for aerial photography is proposed, which is divided into two sub-problems: coverage path planning and formation control. In the coverage path planning, due to high repetition rate and excessive number of turns of the traditional PRM algorithm, the constraint of the number of turns is introduced, and the back-and-forth mode is used to improve it. In formation control, in order to more accurately evaluate the performance of the coverage path, it refers to the consensus theory and combines the artificial potential field method to guide the reverse-V UAV formation to follow the coverage path. Multi-group simulations show that the proposed algorithm is feasible to implement the concentrated coverage for aerial photography under the comprehensive consideration of path length, number of turns, calculation time, coverage rate and repeating rate.

The components detection of a failed satellite is an important work in space on-orbit service. However, the current detection methods for failed satellite components do not consider the effects of low illumination and small targets on the detection accuracy of components at the same time, and most of the datasets used are manually designed. This article proposes a detection method based on image enhancement and an improved Faster R-CNN for small components of a failed satellite in low illumination. Firstly, the dataset of failed satellite components dataset containing low illumination scenarios is established in a simulated real space environment. Secondly, an image enhancement based on reflection model and principal component analysis is proposed, which further enhances images while conserving richer details. Finally, an improved Faster R-CNN for small components is proposed. To improve the detection accuracy of small components, the original Faster R-CNN is improved by modifying three modules: backbone, region proposal network, and RoI pooling layer respectively (i.e., M-HRNet, IoU-balanced sampling, RoI Align). Experimental results show that the proposed method can accurately detect all the small components on satellite capture plane, and the detection performance for low illumination and small components is improved significantly compared to the state-of-the-art methods and original Faster R-CNN.

The detection and segmentation of key spacecraft components is a crucial prerequisite for the successful execution of on-orbit capture tasks. However, the existing spacecraft datasets are plagued by several problems. These include a lack of a unified dataset for component detection and segmentation, a lack of consideration of the motion states of the spacecraft, and a lack of consideration of extreme illuminations. These problems hinder the development of related research. In response to the above problems, this paper aims to bridge this gap by releasing a dataset for detecting and segmenting key spacecraft components. In contrast to synthetic images, the proposed dataset is built via hardware-in-the-loop capture. The dataset contains 2 types of spacecrafts, 9 types of key components, 3 types of illuminations (normal illumination, low illumination, and high saturation), and 2 types of motion states (approaching phase and hovering phase). Furthermore, we conduct extensive experiments on Spacecraft-DS with state-of-the-art methods for detection and segmentation tasks and provide comprehensive analysis. In addition, the dataset can also be used to evaluate many computer vision tasks, such as small object detection, etc. Our dataset and pre-trained models will be released at https://github.com/spacecraftds/Spacecraft-DS.

Dynamic targets in the environment can seriously affect the accuracy of simultaneous localization and mapping (SLAM) systems. This article proposes a novel dynamic visual SLAM method with inertial measurement unit (IMU) and deep learning for indoor dynamic blurred scenes, which improves the front end of ORB-SLAM2, combining deep learning with geometric constraint to make the dynamic feature points elimination more reasonable and robust. First, a multi-directional superposition blur augmentation algorithm is added to the YOLOv5s network to compensate for errors caused by fast-moving targets, camera shake and camera focus. Then, the fine-tuned YOLOv5s model is used to detect potential dynamic regions. Afterward, IMU measurements are introduced for rotation compensation to calculate the feature point velocity and estimate the motion speed of the camera, in order to estimate the real motion state of potential dynamic targets. Finally, real dynamic points will be removed and potential dynamic points will be reserved for subsequent pose estimation. Experiments are conducted on Technische Universität München dynamic dataset and in the real world. The results demonstrate that the proposed method has significant improvement compared with ORB-SLAM2, and has a more robust performance over some other state-of-the-art dynamic visual SLAM systems.

With the increasing demand for marine monitoring, the use of coverage path planning based on unmanned aerial vehicle (UAV) aerial images to assist multiple unmanned surface vehicles (USVs) has shown great potential in marine applications. However, achieving accurate map modeling and optimal path planning are still key challenges that restrict its widespread application. To this end, an innovative coverage path planning algorithm for UAV-assisted multiple USVs is proposed. First, a semantic segmentation algorithm based on the YOLOv5-assisted prompting segment anything model (SAM) is designed to establish an accurate map model. By refining the axial, length, width, and coordinate information of obstacles, the algorithm enables YOLOv5 to generate accurate object bounding box prompts and then assists SAM in automatically and accurately extracting obstacles and coastlines in complex scenes. Based on this accurate map model, a multi-objective stepwise optimization coverage path planning algorithm is further proposed. The algorithm divides the complete path into two parts, the straight paths and the turning paths, and both the path length and the number of turns is designed, respectively, to optimize each type of path step by step, which significantly improves the coverage effect. Experiments prove that in various complex marine coverage scenarios, the proposed algorithm achieves 100% coverage, the redundancy rate is less than 2%, and it is superior to existing advanced algorithms in path length and number of turns. This research provides a feasible technical solution for efficient and accurate marine coverage tasks and lays the foundation for unmanned marine supervision.

The overall safety of a building can be effectively evaluated through regular inspection of the indoor walls by unmanned ground vehicles (UGVs). However, when the UGV performs line patrol inspections according to the specified path, it is easy to be affected by obstacles. This paper presents an obstacle avoidance strategy for unmanned ground vehicles in indoor environments. The proposed method is based on monocular vision. Through the obtained environmental information in front of the unmanned vehicle, the obstacle orientation is determined, and the moving direction and speed of the mobile robot are determined based on the neural network output and confidence. This paper also innovatively adopts the method of collecting indoor environment images based on camera array and realizes the automatic classification of data sets by arranging cameras with different directions and focal lengths. In the training of a transfer neural network, aiming at the problem that it is difficult to set the learning rate factor of the new layer, the improved bat algorithm is used to find the optimal learning rate factor on a small sample data set. The simulation results show that the accuracy can reach 94.84%. Single-frame evaluation and continuous obstacle avoidance evaluation are used to verify the effectiveness of the obstacle avoidance algorithm. The experimental results show that an unmanned wheeled robot with a bionic transfer-convolution neural network as the control command output can realize autonomous obstacle avoidance in complex indoor scenes.