Satellite mission planning is the basis and top-level work of space missions and the beginning of each space mission. Therefore, the scientific research of satellite mission planning is very important. By analyzing the existing research results, we can know that the research on task planning mainly focuses on three aspects: research objects, established model, and solution algorithm. Starting from these three aspects vertically and then horizontally, this paper comprehensively discusses the theoretical basis, application, and advantages and disadvantages of related technologies in the research literature in recent years. Finally, based on the research on satellite mission planning, this paper puts forward its own views on the future development direction and research focus.

Space modular self-reconfigurable satellite (SMSRS) is a new type of satellite. The research on self-collision avoidance of SMSRS is important for its on-orbit safety but is not completely solved. This paper offers a new method for joint path planning for self-collision avoidance of SMSRS. Firstly, we establish the collision detection model for SMSRS based on forward kinematics and the spherical nonholonomic envelope to detect the collision occurring in SMSRS. Then, to achieve offline path planning in joint space, we proposed the self-collision avoidance strategy, which splices multiple C-spaces based on the pre-defined joint path into a binary map, and then transforms the binary map into a map with the dangerous potential field, and planning algorithms based on a map with the dangerous potential field is proposed to find the optimal collision-free path. The new method is applied to two cases and both find collision-free joint paths for SMSRS successfully, which demonstrates the feasibility of the method. In addition, this study bridges the gap in the study of self-collision avoidance of super-redundant self-reconfigurable satellites.

The realization of microsatellite intelligent mission planning is the current research focus in the field of satellite planning, and mission schedulability prediction is the basis of this research. Aiming at the influence of the sequence tasks before and after the task sequence to be predicted, we propose an online schedulability prediction model of satellite tasks based on bidirectional long short-term memory (Bi-LSTM) on the basis of describing and establishing the satellite task planning and solving model. The model is trained using satellite offline mission planning data as learning samples. In the experiment, the prediction effect of the model is excellent, with a recall rate of 93.17% and a precision rate of 92.59%, which proves that the model can be effectively applied to predict the schedulability of satellite tasks.

The modular self-reconfigurable satellites (MSRSs) are a new type of satellite that can transform configuration in orbit autonomously. The inverse kinematics of MSRS is difficult to solve by conventional methods due to the hyper-redundant degrees of freedom. In this paper, the kinematic model of the MSRS is established, and the inverse kinematic of the MSRS is transformed into an optimal solution problem with minimum pose error and minimum energy consumption. In order to find the inverse kinematic exact solution, the refractive opposition-based learning and Cauchy mutation perturbation improved differential evolutionary algorithm (RCDE) is proposed. The performance of the algorithm was examined using benchmark functions, and it was demonstrated that the accuracy and convergence speed of the algorithm were significantly improved. Three typical cases are designed, and the results demonstrate that the optimization method is effective in solving the MSRS inverse kinematics problem.