论文标题
时间最佳，恒定加速的神经表示
Neural representation of a time optimal, constant acceleration rendezvous
论文作者
论文摘要
我们训练神经模型以代表最佳策略（即最佳推力方向）和值函数（即飞行时间）的时间最佳，恒定的加速度低敏感性集合。在这两种情况下，我们都会开发并利用数据增强技术，称为最佳示例的向后生成。因此，我们能够与大型数据集生产和合作，并充分利用采用深度学习框架的好处。在所有情况下，我们都取得了成功的精确度，从而成功进行了集合（按照学习的策略模拟）和飞行预测的时间（使用学习的值函数）。我们发现，剩余数量小至几m/s，因此在航天器导航$ΔV$预算的可能性范围内是可以实现的。我们还发现，平均而言，完全误差可以预测从小行星带中任何轨道到地球状轨道上的任何轨道的最佳飞行时间很小（小于4 \％），因此对于实践用途（例如，在初步任务设计阶段）也很感兴趣。
We train neural models to represent both the optimal policy (i.e. the optimal thrust direction) and the value function (i.e. the time of flight) for a time optimal, constant acceleration low-thrust rendezvous. In both cases we develop and make use of the data augmentation technique we call backward generation of optimal examples. We are thus able to produce and work with large dataset and to fully exploit the benefit of employing a deep learning framework. We achieve, in all cases, accuracies resulting in successful rendezvous (simulated following the learned policy) and time of flight predictions (using the learned value function). We find that residuals as small as a few m/s, thus well within the possibility of a spacecraft navigation $ΔV$ budget, are achievable for the velocity at rendezvous. We also find that, on average, the absolute error to predict the optimal time of flight to rendezvous from any orbit in the asteroid belt to an Earth-like orbit is small (less than 4\%) and thus also of interest for practical uses, for example, during preliminary mission design phases.
