Neural representation of a time optimal, constant acceleration rendezvous

TL;DR

This paper trains neural networks to accurately model the optimal rendezvous policy and time of flight in low-thrust space missions, demonstrating high precision and practical utility for mission planning.

Contribution

It introduces a data augmentation technique called backward generation of optimal examples to effectively train neural models for space rendezvous optimization.

Findings

Achieved velocity residuals of a few m/s at rendezvous.

Predicted time of flight with less than 4% error across asteroid belt to Earth.

Demonstrated the practical applicability of neural models in mission design.

Abstract

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 $\Delta V$ 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.