# A Satellite Task Planning Algorithm Based on a Symmetric Recurrent Neural Network
## 1 Introduction
### Task Planning for Satellite Earth Observation  
Intelligent satellites are oriented toward future space-based information network applications and have more comprehensive space observation requirements than traditional satellites. Therefore, Earth observation capability has become one of the core capabilities that must be considered in relation to the development of intelligent satellites. The observation process of the traditional satellite is roughly as follows: first, the imaging request is given; the imaging satellite ground task observation system is planned, according to the obtained imaging task attribute, satellite information and corresponding constraint conditions; then, the remote sensor control command is generated, according to the result. After the confirmation is found to be correct, instructions are sent to the satellite, and the instructions are executed by the satellite. After the image is transmitted, processed, etc., the image is handed over to the user to complete all the processes. The command is executed by the satellite, the image is sent to the user, after downloading and processing, and all of the processes are completed. Moreover, this \"ground planning, on-board execution\" approach manages in-orbit satellites and relies too much on the manual development of detailed task execution plan sequences. The amount of command data required for satellites to be greatly increased and the pressure on the ground monitoring and control stations to control the orbiting satellites continue to grow, and they will not be able to adapt to the increasingly precise and complex observation task requirements and high dynamics of intelligent satellite applications. The autonomous satellite observation process is shown in Figure 1.  
Due to the manpower consuming and error-prone way of formulating detailed instructions, as well as the development of artificial intelligence technology and space science and technology, autonomous task planning has become one of the satellite intelligent control technologies that many countries have attached importance to in recent years. Autonomous task planning refers to the process in which a satellite, after receiving high-level instructions sent from the ground to describe the task requirements, generates the necessary action sequence with a time stamp in-orbit, according to the current state of the satellite, the action set involved in the satellite task and the flight rules through a certain programming solution algorithm. Satellites with autonomous task planning capabilities can perform various routine operations without interference and have the ability to adapt to fault conditions or unknown environments, self-tolerance, and self-recovery. With the introduction and development of iSAT technology, we will face the rapid growth of users and tasks, continuous and irregular tasks, the dynamic access of ground stations, and a complex computing environment in the satellite. How to maximize the use of satellites and measurement and control resources to meet the needs of dynamically changing task sets is an important reason for studying satellite task planning. At present, research on the autonomous observation task planning on satellites has achieved certain results, mainly using heuristic search algorithms. However, the application of machine learning methods to satellite autonomous task planning is still in the exploratory stage. Combining the neural network method and heuristic search algorithm, this paper proposes a task planning algorithm, which is superior to the existing heuristic search algorithm in terms of the overall profit. The rest of this paper is organized as follows. Section 2 reviews the existing satellite task planning algorithms and the application of neural networks in satellite task planning. Section 3 describes the problem of satellite in-orbit observation task planning and sets up the research hypotheses and constraints. In Section 4,  
Figure 1: Autonomous satellite observation process.