Benjamin Donitz | Searcharxiv

arXiv2023

Interstellar Object Uncertainty Evolution and Effect on Fast Flyby Delivery and Required Delta-V

Interstellar objects (ISOs) are small bodies that can travel through our solar system from other star systems. When present in our solar system, they represent an opportunity to study the properties and origins of these objects, as well as the potential for cross-pollination of material between star systems. With current propulsion technology, rendezvous with these objects is likely infeasible, and thus the maximum science return results from a rapid response flyby and impactor. However, while trajectories to ISOs may be feasible, their potentially high ephemeris uncertainties and high-speed hyperbolic orbits present significant challenges to navigation. In this paper we assess these challenges by modeling the uncertainties of reachable synthetic ISOs as a function of time, as derived by measurements from ground observatories and an approaching spacecraft. From these uncertainties we derive the final delivery accuracy of fast flyby spacecraft to the ISO and required statistical delta-v for navigation. We find that these two challenges can lead to hundreds of meters-per-second or even kilometers-per-second of required statistical delta-v for navigation, reduce delivery accuracy to hundreds of kilometers, and make autonomous navigation a requirement.

astro-ph.EP↗

arXiv2022

Neural-Rendezvous: Provably Robust Guidance and Control to Encounter Interstellar Objects

Interstellar objects (ISOs) are likely representatives of primitive materials invaluable in understanding exoplanetary star systems. Due to their poorly constrained orbits with generally high inclinations and relative velocities, however, exploring ISOs with conventional human-in-the-loop approaches is significantly challenging. This paper presents Neural-Rendezvous -- a deep learning-based guidance and control framework for encountering fast-moving objects, including ISOs, robustly, accurately, and autonomously in real time. It uses pointwise minimum norm tracking control on top of a guidance policy modeled by a spectrally-normalized deep neural network, where its hyperparameters are tuned with a loss function directly penalizing the MPC state trajectory tracking error. We show that Neural-Rendezvous provides a high probability exponential bound on the expected spacecraft delivery error, the proof of which leverages stochastic incremental stability analysis. In particular, it is used to construct a non-negative function with a supermartingale property, explicitly accounting for the ISO state uncertainty and the local nature of nonlinear state estimation guarantees. In numerical simulations, Neural-Rendezvous is demonstrated to satisfy the expected error bound for 100 ISO candidates. This performance is also empirically validated using our spacecraft simulator and in high-conflict and distributed UAV swarm reconfiguration with up to 20 UAVs.

cs.RO↗