Lightweight compact optical correlator for spacecraft docking
Bibliographic record
Abstract
Spacecraft docking, landing and star tracking are critical operations in various space missions. Docking provides the opportunity to joint two vehicles in order to change crews and deliver resources to a spacecraft. One of the main challenges in docking is to perform real-time tracking of the docking point for a precise and rapid feedback to the control system in order to achieve reliable operations. The same requirements are found for landing operations and star-tracking with main difference that the ground or sky is used for position and attitude tracking. Docking operations found multiple earth counterpart applications. Many of these earth-based applications concern the use of robotic devices to grab a specific object. In these cases various location parameters of the object are needed, such as rotation angle, scale and position. INO has developed a compact lightweight optical correlator prototype. This prototype provides a tool for the evaluation of various applications. In collaboration with ESA, INO studied the use of an optical correlator for selected space applications such as rendez-vous and docking, landing and star tracking operations. Optical correlator provides beyond real-time image processing capabilities and is well suited for target identification and positioning purpose. The optical correlator also shows low power consumption. In this paper, the latest analyses of the docking and landing applications are presented. For evaluation purpose, video sequences of Soyuz docking the International Space Station (ISS) were used. In the case of landing, moon images acquired in the SMART-1 mission, during its last orbits, were used. Mt. Wilson telescope images were used for star tracking examples.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".