During the on-orbit operation task of the space manipulator, some specific scenarios require strict constraints on both the position and orientation of the end-effector, such as refueling and auxiliary docking. To this end, a novel motion planning approach for a space manipulator is proposed in this paper. Firstly, a kinematic model of the 7-DOF free-floating space manipulator is established by introducing the generalized Jacobian matrix. On this basis, a planning approach is proposed to realize the motion planning of the 7-DOF free-floating space manipulator. Considering that the on-orbit environment is dynamical, the robustness of the motion planning approach is required, thus the deep reinforcement learning algorithm is introduced to design the motion planning approach. Meanwhile, the deep reinforcement learning algorithm is combined with artificial potential field to improve the convergence. Besides, the self-collision avoidance constraint is considered during planning to ensure the operational security. Finally, comparative simulations are conducted to demonstrate the performance of the proposed planning method.

The pose determination between nanosatellites and the cooperative spacecraft is essential for swarm in-orbit services. Time-of–flight (ToF) sensors are one of the most promising sensors to achieve the tasks. This paper presented an end-to-end assessment of how these sensors were used for pose estimation. First, an embedded system was designed based on the ToF camera with lasers as a driven light source. Gray and depth images were collected to detect and match the cooperative spacecraft in real time, obtaining the pose information. A threshold-based segmentation was proposed to find a small set of the pixels belonging to reflector markers. Only operating on the defined active pixel set reduced computational resources. Then, morphological detection combined with an edge following-based ellipse detection extracted the centroid coordinate of the circular marker, while the center-of-heart rate was calculated as the recognition condition. Next, the marker matching was completed using a deterministic annealing algorithm, obtaining two sets of 3D coordinates. A singular value decomposition (SVD) algorithm estimated the relative pose between the nanosatellite and the spacecraft. In the experiments, the pose calculated by the TOF camera reached an accuracy of 0.13 degrees and 2 mm. It accurately identified the markers and determined the pose, verifying the feasibility of the ToF camera for rendezvous and docking.

Generative adversarial networks (GANs) are able to produce realistic images. However, GANs may suffer mode collapse in their output data distribution. Here, we theoretically and empirically justify generalizing the GAN framework to multiple discriminators with one generator for improving generative performance. First, a comprehensive perspective is adopted to understand why mode collapse occurs. Second, an array of cooperative realness discriminators is introduced into the GAN framework to combat mode collapse and explore discriminator roles ranging from a formidable adversary to a forgiving teacher. Third, two types of simple yet effective regularization are proposed for generating realistic and diverse images. Experiments on various datasets show the effectiveness of the GAN compared to previous methods in alleviating mode collapse and improving the quality of the generated samples.

The space environment has become highly congested due to the increasing space debris, seriously threatening the safety of orbiting spacecraft. Space-based situational awareness, as a comprehensive capability of threat knowledge, analysis, and decision-making, is of significant importance to ensure space security and maintain normal order. Various space situational awareness systems have been designed and launched. Data acquisition, target recognition, and monitoring constituting key technologies make major contributions, and various advanced algorithms are explored as technical supports. However, comprehensive reviews of these technologies and specific algorithms rarely emerge. It disadvantages the future development of space situational awareness. Therefore, this paper further reviews and analyzes research advancements in key technologies for space situational awareness, emphasizing target recognition and monitoring. Many mature and emerging methods are presented for these technologies while discussing application advantages and limitations. Specially, the research prospects of multiagent and synergetic constellation technologies are expected for future situational awareness. This paper indicates the future directions of the key technologies, aiming to provide references for space-based situational awareness to realize space sustainability.