# A Satellite Task Planning Algorithm Based on a Symmetric Recurrent Neural Network
## 2 Related Research  
### Overview of Satellite Task Planning Algorithms  
The task planning problem of intelligent satellites is the same as that of traditional satellites. It is essentially an NP-hard constraint satisfaction problem [12]. In other words, with the goal of satisfying on-board resource constraints and task constraints, the way to arrange the execution sequence and specific execution time of a group of tasks, so as to make one or more objective functions, such as the largest observation area, the largest number of images, the highest energy distribution efficiency, and the largest task income reach, the optimum is determined. The on-board resource constraints include electrical energy, mobility, communication bandwidth, storage space, side viewing capabilities, and so on. The task constraints include the target area (point and face) size and position, sun height angle, target line of the sight angle, and so on. Many researchers have now studied autonomous satellite task planning and tend to use intelligent optimization algorithms and heuristic algorithms to solve them. The intelligent optimization algorithm does not need to construct an accurate mathematical search direction and does not need to perform complicated one-dimensional search. Instead, it obtains the optimal solution of the problem through a large number of simple information dissemination and evolution methods, including the genetic algorithm, simulated annealing algorithm, tabu search algorithms, etc. [13]. [PERSON] et al. [14] studied the task planning problem of optical imaging satellites using the tabu search algorithm and divided the process of the solution into stages. [PERSON] et al. [15] also designed an adaptive mutation probability genetic algorithm, when studying the task planning problem of Earth observation satellites. [PERSON] et al. [16], in a study of low-orbit satellite task planning, took task priority, resource constraints and user satisfaction into consideration and designed a new tabu search algorithm to solve them. [PERSON] et al. [17] adopted the greedy algorithm for task planning and solving. [PERSON] et al. [18] used the simulated annealing algorithm to solve the satellite point target observation task planning problem. [PERSON] et al. [19] designed a combination algorithm to solve the problem of task planning for an earth observation satellite in order to avoid the instability problem associated with providing a solution using a single traditional algorithm. In order to improve the efficiency of remote sensing satellite scheduling, [PERSON] et al. [20] designed the ant colony algorithm for remote sensing satellite ground integrated task planning. [PERSON] and [PERSON] [21] designed an adaptive ant colony algorithm by designing a task merging mechanism to solve the problem of huge user requests. [PERSON] et al. [22] combined the re-planning technique with the multi-objective hybrid dynamic mutation genetic algorithm for dynamic re-planning scenarios of different urgency levels and designed the cyclic re-planning algorithm and the near-real-time re-planning algorithm. [PERSON] [23] used genetic algorithms to solve dynamic task planning problems. The heuristic algorithm provides a feasible solution to the combinatorial optimization problem, with an acceptable cost (calculation time, space occupation, etc.). The degree of deviation of the feasible solution from the optimal solution is generally not expected. With the deepening of research, heuristic algorithms can also be simple or complex, single or mixed. It has gradually become an effective method for solving combinatorial optimization problems. [PERSON] et al. [24] regards satellite task planning as an integer programming problem. Using a non-standard blackmail heuristic algorithm, a heuristic algorithm based on Lagrangian relaxation is proposed to solve the problem and verify the efficiency of the algorithm. [PERSON] et al. [25] proposed a local search algorithm based on heuristic rules. [PERSON] et al.