Space shuttle testing of the TriDAR 3D rendezvous and docking sensor
Bibliographic record
Abstract
Abstract This paper presents results from the first two Space Shuttle test flights of the TriDAR vision system. TriDAR was developed as a proximity operations sensor for autonomous rendezvous and docking (AR&D) missions to noncooperative targets in space. The system does not require the use of cooperative markers, such as retro‐reflectors, on the target spacecraft. TriDAR includes a hybrid three‐dimensional (3D) sensor along with embedded model based tracking algorithms to provide six‐degree‐of‐freedom (6 DOF) relative pose information in real time. A thermal imager is also included to provide range and bearing information for far‐range rendezvous operations. In partnership with the Canadian Space Agency (CSA) and NASA, Neptec has space‐qualified the TriDAR vision system and integrated it on board Space Shuttle Discovery to fly as a detailed test objective (DTO) on the STS‐128 and STS‐131 missions to the International Space Station (ISS). The objective of the TriDAR DTO missions was to demonstrate the system's ability to perform acquisition and tracking of a known target in space autonomously and provide real‐time relative navigation cues. Knowledge (reference 3D model) about the target can be obtained on the ground or in orbit. Autonomous operations involved automatic acquisition of the ISS and real‐time tracking, as well as detection and recovery from system malfunctions and/or loss of tracking. © 2012 Wiley Periodicals, Inc.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".