Robust learning for collision-free trajectory in space environment with limited a priori information. (October 2021)

• Record Type: Journal Article
• Authors: Ge, Dantong; Chu, Xiaoyu
• Is Part Of: Acta astronautica. Volume 187(2021)
• Page Start: 281
• Page End: 294
• Publication Date: 2021-10
• Subjects: Trajectory planning -- Obstacle avoidance -- Environment uncertainties -- Robust learning; Astronautics -- Periodicals; Outer space -- Exploration -- Periodicals; Astronautics; Periodicals; 629.405
• DOI: 10.1016/j.actaastro.2021.06.038
• Languages: English
• ISSNs: 0094-5765

Abstract

Abstract: For on-orbit repair and space debris removal missions, a collision-free trajectory to the target is to be planned in a dynamically changing environment. Although such a trajectory can be learned in a ground simulator through repetitive trials, the established environment is often of limited precision due to space perturbations and measurement errors. Considering possible discrepancies between simulation and the real world, a tunnel, instead of a single trajectory, should be learned on the ground. In this paper, a robust planning method based on Q-learning is proposed for space missions with a priori environment information of limited precision. Based on a specific on-orbit repair scenario, reward functions in accordance with the multiple mission objectives are designed. The trajectories learned under different parameter randomization settings are combined and a robust tunnel is generated in the discrete grid world. By keeping the spacecraft inside the tunnel in the actual mission, collisions with the dynamic obstacles would be avoided and the goal of target rendezvous would be achieved. At last, a numerical simulation is carried out and the proposed method is validated under both nominal and randomized conditions. Highlights: Q-learning is utilized to find a safe trajectory to the target in space missions. Reward functions taking into account multiple mission objectives are designed. Possible discrepancies between simulation and the real world are considered. A collision-free trajectory tunnel is learned with guaranteed robustness. Both nominal and randomized conditions are validated in the simulation.