Spacecraft relative trajectory planning is central to many space missions like on-orbit service and debris removal. While early attempts of machine-learning-based trajectory planning have been witnessed, they still face the open problem of how to acquire sufficient training samples to conduct robust training. In this article, we have introduced a meta-learning framework to improve the adaptation ability of the planner when facing new initial conditions. To achieve this goal, we divided the training trajectories as sub-training samples and fake testing samples. Then, the meta planner is trained by repeatedly conducting a trail training-and-testing process. To this end, the gradient information of the meta learner is initially obtained on the subtraining sets and is further adjusted by looking at its testing performance on those fake testing data. Therefore, the meta planer explicitly take account of the potential testing performance into consideration and, hence, alleviate the overfitting phenomena with few training trajectories. Simulation results substantiate the effectiveness of our approach, as well as the advantages of quick adaptation to new initial conditions without overfitting.

中文翻译：

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基于元学习的航天器相对轨迹规划


航天器相对轨迹规划对于许多太空任务（如在轨服务和碎片清除）至关重要。虽然基于机器学习的轨迹规划的早期尝试已经出现，但它们仍然面临着如何获取足够的训练样本来进行稳健训练的开放问题。在本文中，我们引入了一个元学习框架来提高规划器在面对新的初始条件时的适应能力。为了实现这一目标，我们将训练轨迹分为子训练样本和假测试样本。然后，通过重复进行试验训练和测试过程来训练元规划器。为此，元学习器的梯度信息最初是在子训练集上获得的，并通过查看其在那些假测试数据上的测试性能来进一步调整。因此，元规划器明确考虑了潜在的测试性能，从而用很少的训练轨迹缓解了过度拟合现象。仿真结果证实了我们方法的有效性，以及快速适应新初始条件而不会过度拟合的优点。