Remote sensing instrumentation on-board missions to asteroids is paramount to address many of the fundamental questions in modern planetary science. Yet, in-situ surface measurements provide the "ground-truth" necessary to validate and enhance the science return of these missions. Nevertheless, due to the dynamical uncertainties associated with the environment near these objects, most missions spend long periods of times stationed afar. Small landers can be used much more daringly, however, and thus have already been identified as valuable assets for insitu exploration. This paper explores the potential for ballistic landing opportunities enabled by the natural dynamics found in binary asteroid systems. The dynamics near a binary asteroid are modelled by means of the Circular Restricted Three Body Problem, which provides a reasonable representation of a standard binary system. Natural landing trajectories are then sought that allow for a deployment from the exterior region and touchdown with minimum local-vertical velocity. The results show that while landing on the main body of the system would require an effective landing system capable to dissipate excess of energy, and avoid bouncing off the asteroid, the smaller companion offers the prospect of simple ballistic landing opportunities. Nomenclature a = Semi-major axis of the mutual binary asteroid orbit [km]
The OSIRIS-REx mission collected a sample from the surface of the asteroid (101955) Bennu in October 2020. Here we study the impact of the OSIRIS-REx Touch-and-Go Sampling Acquisition Mechanism (TAGSAM) interacting with the surface of an asteroid in the framework of granular physics. Traditional approaches to estimating the penetration depth of a projectile into a granular medium include force laws and scaling relationships formulated from laboratory experiments in terrestrial-gravity conditions. However, it is unclear that these formulations extend to the OSIRIS-REx scenario of a 1300-kg spacecraft interacting with regolith in a microgravity environment. We studied the TAGSAM interaction with Bennu through numerical simulations using two collisional codes, pkdgrav and GDC-i. We validated their accuracy by reproducing the results of laboratory impact experiments in terrestrial gravity. We then performed TAGSAM penetration simulations varying the following geotechnical properties of the regolith: packing fraction (P), bulk density, inter-particle cohesion (σc), and angle of friction (ϕ). We find that the outcome of a spacecraft-regolith impact has a non-linear dependence on packing fraction. Closely packed regolith (P≳0.6) can effectively resist the penetration of TAGSAM if ϕ≳28° and/or σc≳50 Pa. For loosely packed regolith (P≲0.5), the penetration depth is governed by a drag force that scales with impact velocity to the 4/3 power, consistent with energy conservation. We discuss the importance of low-speed impact studies for predicting and interpreting spacecraft-surface interactions. We show that these low-energy events also provide a framework for interpreting the burial depths of large boulders in asteroidal regolith.
Iterative trajectory optimization techniques for non-linear dynamical systems are among the most powerful and sample-efficient methods of model-based reinforcement learning and approximate optimal control. By leveraging time-variant local linear-quadratic approximations of system dynamics and reward, such methods can find both a target-optimal trajectory and time-variant optimal feedback controllers. However, the local linear-quadratic assumptions are a major source of optimization bias that leads to catastrophic greedy updates, raising the issue of proper regularization. Moreover, the approximate models' disregard for any physical state-action limits of the system causes further aggravation of the problem, as the optimization moves towards unreachable areas of the stateaction space. In this paper, we address the issue of constrained systems in the scenario of online-fitted stochastic linear dynamics. We propose modeling state and action physical limits as probabilistic chance constraints linear in both state and action and introduce a new trajectory optimization technique that integrates these probabilistic constraints by optimizing a relaxed quadratic program. Our empirical evaluations show a significant improvement in learning robustness, which enables our approach to perform more effective updates and avoid premature convergence observed in state-of-the-art algorithms.
The OSIRIS-REx mission collected a sample from the surface of the asteroid (101955) Bennu in 2020 October. Here, we study the impact of the OSIRIS-REx Touch-and-Go Sampling Acquisition Mechanism (TAGSAM) interacting with the surface of an asteroid in the framework of granular physics. Traditional approaches to estimating the penetration depth of a projectile into a granular medium include force laws and scaling relationships formulated from laboratory experiments in terrestrial-gravity conditions. However, it is unclear that these formulations extend to the OSIRIS-REx scenario of a 1300-kg spacecraft interacting with regolith in a microgravity environment. We studied the TAGSAM interaction with Bennu through numerical simulations using two collisional codes, PKDGRAV and GDC-I. We validated their accuracy by reproducing the results of laboratory impact experiments in terrestrial gravity. We then performed TAGSAM penetration simulations varying the following geotechnical properties of the regolith: packing fraction (P), bulk density, inter-particle cohesion (σ c ), and angle of friction (φ). We find that the outcome of a spacecraft-regolith impact has a non-linear dependence on packing fraction. Closely packed regolith (P 0.6) can effectively resist the penetration of TAGSAM if φ 28 • and/or σ c 50 Pa. For loosely packed regolith (P ࣠ 0.5), the penetration depth is governed by a drag force that scales with impact velocity to the 4/3 power, consistent with energy conservation. We discuss the importance of low-speed impact studies for predicting and interpreting spacecraft-surface interactions. We show that these low-energy events also provide a framework for interpreting the burial depths of large boulders in asteroidal regolith.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.