Autonomous Exploration of Small Bodies Toward Greater Autonomy for Deep Space Missions

Author:

Nesnas Issa A. D.,Hockman Benjamin J.,Bandopadhyay Saptarshi,Morrell Benjamin J.,Lubey Daniel P.,Villa Jacopo,Bayard David S.,Osmundson Alan,Jarvis Benjamin,Bersani Michele,Bhaskaran Shyam

Abstract

Autonomy is becoming increasingly important for the robotic exploration of unpredictable environments. One such example is the approach, proximity operation, and surface exploration of small bodies. In this article, we present an overview of an estimation framework to approach and land on small bodies as a key functional capability for an autonomous small-body explorer. We use a multi-phase perception/estimation pipeline with interconnected and overlapping measurements and algorithms to characterize and reach the body, from millions of kilometers down to its surface. We consider a notional spacecraft design that operates across all phases from approach to landing and to maneuvering on the surface of the microgravity body. This SmallSat design makes accommodations to simplify autonomous surface operations. The estimation pipeline combines state-of-the-art techniques with new approaches to estimating the target’s unknown properties across all phases. Centroid and light-curve algorithms estimate the body–spacecraft relative trajectory and rotation, respectively, using a priori knowledge of the initial relative orbit. A new shape-from-silhouette algorithm estimates the pole (i.e., rotation axis) and the initial visual hull that seeds subsequent feature tracking as the body gets more resolved in the narrow field-of-view imager. Feature tracking refines the pole orientation and shape of the body for estimating initial gravity to enable safe close approach. A coarse-shape reconstruction algorithm is used to identify initial landable regions whose hazardous nature would subsequently be assessed by dense 3D reconstruction. Slope stability, thermal, occlusion, and terra-mechanical hazards would be assessed on densely reconstructed regions and continually refined prior to landing. We simulated a mission scenario for approaching a hypothetical small body whose motion and shape were unknown a priori, starting from thousands of kilometers down to 20 km. Results indicate the feasibility of recovering the relative body motion and shape solely relying on onboard measurements and estimates with their associated uncertainties and without human input. Current work continues to mature and characterize the algorithms for the last phases of the estimation framework to land on the surface.

Publisher

Frontiers Media SA

Subject

Artificial Intelligence,Computer Science Applications

Reference74 articles.

1. An Architecture for Autonomy;Alami;Int. J. Robotics Res.,1998

2. Fast Explicit Diffusion for Accelerated Features in Nonlinear Scale Spaces;Alcantarilla;IEEE Trans. Patt. Anal. Mach. Intell.,2011

3. The Power Crust;Amenta,2001

4. Silhouette-based 3d Shape Reconstruction of a Small Body from a Spacecraft;Bandyopadhyay,2019

5. Light-robust Pole-From-Silhouette Algorithm and Visual-hull Estimation for Autonomous Optical Navigation to an Unknown Body;Bandyopadhyay,2021

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Onboard Science Instrument Autonomy for the Detection of Microscopy Biosignatures on the Ocean Worlds Life Surveyor;The Planetary Science Journal;2024-01-01

2. Early Design Exploration of Space System Scenarios Using Assume-Guarantee Contracts;2023 IEEE 9th International Conference on Space Mission Challenges for Information Technology (SMC-IT);2023-07

3. Autonomica: Ontological Modeling and Analysis of Autonomous Behavior;INCOSE International Symposium;2023-07

4. Extracting Orbital Information from the Attitude Control System of a Spacecraft near Small Bodies;2023 IEEE Aerospace Conference;2023-03-04

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3