Optimal Capture of Spinning Spacecraft via Deep Learning Vision and Guidance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it