Learning-based spacecraft multi-constraint rapid trajectory planning for emergency collision avoidance

Aim at the emergency collision avoidance scenarios caused by the close-range space debris, a learning-based spacecraft rapid trajectory planning method, which can adapt to complex constraints and satisfy the requirements of the business service and real-time replanning, is proposed in this paper. First, the emergency collision avoidance scenarios are initialized and the optimal multi-constraint avoidance trajectories are generated based on the Gauss pseudo-spectral method with corresponding feasibility checks. Then, taking the generated collocation points as the initial guess, the new trajectories are regenerated by finetuning scenarios. When the trajectory data is collected to some extent, the scenarios will be reset. The “state-action” data set can be established and extended by the above “plan-check-finetune-reset” loops. On this basis, two types of “state-action” neural networks and the corresponding supervised training method are specially designed based on ideas of multi-layer and bidirectional long short-term memory networks by considering continuous-discrete hybrid control characteristics of spacecraft actuators and interdependent temporal logical relationships in the data generated by dynamic differential equations. The designed networks are trained based on the established data set. Finally, spacecraft attitude-orbit maneuvering instructions can be resolved in real-time by trained networks according to the perceptive information for space debris. Simulation results show that the runtime of the proposed method in each step can be maintained within 10.4 ms, and the overall avoidance success rate can reach 87.6 % in Monte Carlo test conditions.