Robust trajectory maneuver scheduling near the flyby of small celestial bodies on the basis of proximal policy optimization

Owing to the large communication delay in deep space exploration missions, trajectory maneuvers prior to the flyby of small celestial bodies generally need to be scheduled in advance. However, the lack of prior data and the presence of environmental uncertainties in deep space are significant challenges for maneuver scheduling. To solve this problem, in this study, robust maneuver scheduling networks based on proximal policy optimization were proposed. A reward function that considers the terminal state accuracy of the spacecraft after maneuvering and the total velocity impulse cost was designed for the maneuver scheduling networks. An additional constant was added to the variance of the actor network to improve the performance of the generated maneuvering strategy. Compared with the actor–critic algorithm and genetic algorithm, the maneuvering strategy generated by the maneuver scheduling networks demonstrated the best performance in most simulation scenarios and maintained a better balance between the terminal state accuracy and the total velocity impulse cost. The robustness of the maneuver strategy against uncertain perturbations in the environment and uncertain initial state deviations of the spacecraft was validated in several maneuver scenarios in the simulation. In addition, the generated maneuvering strategy exhibited excellent real-time performance. The time cost to make a decision was still better than 0.7 s in the worst case, testing on Raspberry Pi 4B with a memory of 4 GB and a limited CPU frequency of 800 MHz. The robustness against uncertainties and real-time capability of the proposed method revealed its potential onboard application to future deep space exploration missions.