HackerPapers

arXiv:2307.11226eess.SY

BLISS: Interplanetary Exploration with Swarms of Low-Cost Spacecraft

Alexander N. Alvara, Lydia Lee, Emmanuel Sin, Nathan Lambert, Andrew J. Westphal, Kristofer S. J. Pister

HN submission. Found in a submission link or body. Submitters do not count as commenters in the main ranking; a submission is not an endorsement.

Abstract

Leveraging advancements in micro-scale technology, we propose a fleet of autonomous, low-cost, small solar sails for interplanetary exploration. The Berkeley Low-cost Interplanetary Solar Sail (BLISS) project aims to utilize small-scale technologies to create a fleet of tiny interplanetary femto-spacecraft for rapid, low-cost exploration of the inner solar system. This paper describes the hardware required to build a nearly 10 g spacecraft using a 1 m$^2$ solar sail steered by micro-electromechanical systems (MEMS) inchworm actuators. The trajectory control to a NEO, here 101955 Bennu, is detailed along with the low-level actuation control of the solar sail and the specifications of proposed onboard communication and computation. Two other applications are also shortly considered: sample return from dozens of Jupiter-family comets and interstellar comet rendezvous and imaging. The paper concludes by discussing the fundamental scaling limits and future directions for steerable autonomous miniature solar sails with onboard custom computers and sensors.

arXiv comments
16 pages, 13 figures, 5 tables, 23 equations, and just over 10 years
Journal reference
DOI
Subjects
eess.SYSystems and Control (EESS) eess.IVImage and Video Processing physics.space-phSpace Physics

On Hacker News

No comments linking this paper were found in the collected sources. Found in 1 HN submission from Jul 2023 to Jul 2023. Open submission discussions.

Comments linking this paper, per year.

Discussed in comments0 threads

No comments linking this paper were found in the collected sources. Open submission discussions.

Submitted to Hacker News

Connected through shared HN threads, not inferred similarity or a recommendation.