Intelligent mission planning method for on-orbit service of high-orbit spacecraft cluster

A mission planning model for on-orbit service of high-orbit spacecraft with two optimization objectives, fuel consumption and time consumption, is developed for the high-orbit spacecraft multi-to-multi on-orbit service mission planning. And the Q-learning-based Multi-objective Genetic Algorithm (QMGA) is proposed to solve the model. Firstly, a multi-to-multi objective assignment model based on four-impulse Lambert transfer is established, The velocity impulse consumption and time consumption are taken the objective functions. By decoupling the problem into the orbit transfer optimization problem and the target assignment optimization problem, the dimension of the optimization variables is reduced, and the calculation process is simplified. Then, combined with Q-learning, the QMGA algorithm is proposed. The Q-learning is used to update the crossover probability and mutation probability of the multi-objective genetic algorithm, which improves the optimization ability of the algorithm, Finally, the QMGA algorithm is adopted to solve the model, and the calculation results are compared with that of the traditional multi-objective genetic algorithm. It is found that the QMGA algorithm can obtain better results and complete multi-to-multi on-orbit service tasks with less fuel consumption in shorter time. The fuel consumption and the time consumption computed with the QMGA algorithm were 6.2% and 19.79% lower than those computed with MGA algorithm on average, respectively. This proves that the reinforcement learning method can further empower the traditional intelligent optimization method, thereby improving the mission capability of the spacecraft cluster.