Artificial Intelligence Aided Rapid Trajectory Design in Complex Dynamical Environments

2019-05-14T15:34:34Z (GMT) by Ashwati Das
<div><div>Designing trajectories in dynamically complex environments is challenging and can easily become intractable via solely manual design efforts. Thus, the problem is recast to blend traditional astrodynamics approaches with machine learning to develop a rapid and flexible trajectory design framework. This framework incorporates knowledge of the spacecraft performance specifications via the computation of Accessible Regions (ARs) that accommodate specific spacecraft acceleration levels for varied mission scenarios in a complex multi-body dynamical regime. Specifically, pathfinding agents, via Heuristically Accelerated Reinforcement Learning (HARL) and Dijkstra's algorithms, engage in a multi-dimensional combinatorial search to sequence advantageous natural states emerging from the ARs to construct initial guesses for end-to-end transfers. These alternative techniques incorporate various design considerations, for example, prioritizing computational time versus the pursuit of globally optimal solutions to meet multi-objective mission goals. The initial guesses constructed by pathfinding agents then leverage traditional numerical corrections processes to deliver continuous transport of a spacecraft from departure to destination. Solutions computed in the medium-fidelity Circular Restricted Three Body (CR3BP) model are then transitioned to a higher-fidelity ephemeris regime where the impact of time-dependent gravitational influences from multiple bodies is also explored.</div><div><br></div><div>A broad trade-space arises in this investigation in large part due to the rich and diverse dynamical flows available in the CR3BP. These dynamical pathways included in the search space via: (i) a pre-discretized database of known periodic orbit families; (ii) flow-models of these families of orbits/arcs `trained' via the supervised learning algorithms Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs); and, finally (iii) a free-form search that permits selection of both chaotic and ordered motion. All three approaches deliver variety in the constructed transfer paths. The first two options offer increased control over the nature of the transfer geometry while the free-form approach eliminates the need for a priori knowledge about available flows in the dynamical environment. The design framework enables varied transfer scenarios including orbit-orbit transport, s/c recovery during contingency events, and rendezvous with a pre-positioned object at an arrival orbit. Realistic mission considerations such as altitude constraints with respect to a primary are also incorporated.</div></div>