New Method and Analysis of Proximity Trajectory-Only Learned Dynamics for Small Body Gravity Fields

Author: Neamati, Daniel A.

Year: 2021

Degree: Senior thesis (Major)

Advisor: Chung, Soon-Jo

Committee Members: Minnich, Austin J.; Chung, Soon-Jo; Hunt, Melany L.; Ehlmann, Bethany L.

Option: Mechanical Engineering; Planetary Sciences

DOI: 10.7907/4csx-4636

Abstract

Recent missions to small bodies in the past decade (e.g., Rosetta, Hayabusa 2, and OSIRIS-REx) have reshaped our understanding of small bodies and inspired new, more-capable future missions. Despite the high demand for more missions, large uncertainties in small body properties make missions challenging. Recent work in stochastic optimal control can ensure safety in the face of uncertainty in state, constraints, and dynamics. These stochastic optimal controllers require a model of the underlying dynamics, which is difficult for proximity maneuvers and landing around small bodies. Shape models and finite element-like models are the state-of-the-art for high-fidelity gravity models, but they are computationally expensive and do not readily incorporate onboard data. No gravity model yet exists that can use short-horizon position and acceleration data from recent trajectories onboard in safety-critical autonomous proximity maneuvers and landing. Therefore, we propose a new trajectory-only learning-based method to develop a gravity model. We consider three learning frameworks: Gaussian Process Models, Neural Networks, and Physics-Informed Neural Networks. For each framework, we assess the benefits, computational costs, and limitations of the framework. We found that the Gaussian Process Model generally outperforms the other frameworks in cases of moderate uncertainty. As the uncertainty declines or the data is sufficiently filtered, Neural Networks with spectral normalization provide more accurate gravity models and are computationally cheaper to evaluate. Lastly, we reflect on the methods in this thesis and recommend possible problem reformulations for future research.

Files