Optimal Capture of Spinning Spacecraft via Deep Learning Vision and Guidance
Bibliographic record
Abstract
This paper addresses the problem of robotic capture of an uncooperative spinning target spacecraft. To do so, a computationally lightweight and real-time implementable guidance, navigation, and control architecture that relies on deep learning as well as pseudospectral optimization is proposed and experimentally validated. Specifically, a convolutional neural-network-driven stereovision pose determination system is first combined with a deep-reinforcement-learning-based guidance algorithm and pose tracking controller to cancel the relative motion between a chaser platform and an uncooperative spinning target platform in real time. Then, real-time tracking of a pseudospectral-based optimal guidance law generated offline deploys a robotic arm while minimizing the overall attitude corrections required to keep the target in view. The integrated experiment carried out using Carleton University’s Spacecraft Proximity Operations Testbed (a state-of-the-art planar air bearing facility, introduced in this work) demonstrates the performance of the developed deep learning architecture.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".