Neural representation of a time optimal, constant acceleration
rendezvous
- URL: http://arxiv.org/abs/2203.15490v1
- Date: Tue, 29 Mar 2022 12:40:50 GMT
- Title: Neural representation of a time optimal, constant acceleration
rendezvous
- Authors: Dario Izzo and Sebastien Origer
- Abstract summary: 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.
We achieve, in all cases, accuracies resulting in successful rendezvous and time of flight predictions.
- Score: 10.191757341020216
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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