Neural representation of a time optimal, constant acceleration rendezvous
This work addresses spacecraft navigation and mission design, particularly for asteroid belt to Earth-like orbit rendezvous, but is incremental as it applies existing deep learning methods with a new data augmentation technique.
The authors tackled the problem of time-optimal, constant acceleration low-thrust rendezvous by training neural models to represent the optimal policy and value function, achieving residuals as small as a few m/s for velocity at rendezvous and an average absolute error of less than 4% for time-of-flight predictions.
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.