Architecture Trades for Accessing Small Bodies with an Autonomous Small Spacecraft
Bibliographic record
Abstract
Characterizing the composition, properties, and environments of Small Bodies is key to understanding the origins and processes of the Solar System. Traditionally, our knowledge has been limited to ground observations and selected few missions which cannot fully characterize the diversity of Small Bodies. Advances in miniaturized spacecraft technologies have recently enabled small spacecraft to perform missions in deep space, as demonstrated by Mars Cube One in 2018. Additional missions are being developed to further mature these technologies and expand their capabilities. We investigate a new approach to exploring Small Bodies, where standalone small spacecraft can be used as a more affordable approach to autonomously navigate, rendezvous, and characterize them. We review relevant mission concepts, available targets, architecture trade-offs, and required technologies for baseline mission design options. Using near-term technologies that will be available in less than 3 years, Our results indicate that it is possible for standalone small spacecraft to rendezvous one of several Small Bodies. It was found that a 24 kg and 180 kg spacecraft would be capable of delivering payloads of 1.5 kg and 10 kg, respectively, to several near-Earth asteroids candidates. With a departure window of 2022 to 2030, the number of available targets for the 24 kg and 180 kg architectures are 9 to 177 and 104 to 1132 respectively. The number of targets depends on additional requirements, such as orbit uncertainty, composition, and diameter. Enabling technologies include high-delta-V (>3 km/s) miniature electric propulsion system, high-efficiency (>100 W/kg) deployable solar arrays, and improved computational hardware and software for autonomy. Advances in miniaturized instruments, high-performance radiation-tolerant avionics, and interplanetary communications systems can also be leveraged. In the long term, a standardized autonomous small spacecraft architecture could enable a fleet of spacecraft to perform a cursory exploration of a representative sample of Small Bodies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".