Space shuttle testing of the TriDAR 3D rendezvous and docking sensor
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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