STS-128 on-orbit demonstration of the TriDAR targetless rendezvous and docking sensor
Bibliographic record
Abstract
Neptec has developed a vision system for autonomous rendezvous and docking in space that does not require the use of cooperative markers, such as retroreflectors, on the target spacecraft. The system uses an active TriDAR 3D sensor along with embedded model based tracking algorithms to provide, out of the box, 6 degree of freedom (6DOF) relative pose information in real-time. The TriDAR (triangulation + LIDAR) sensing technology combines active triangulation and Time-of-Flight (TOF) ranging techniques within a single optical path. This design takes advantage of the complementary nature of these two technologies to provide optimal 3 dimensional data from several kilometers all the way to docking. A thermal imager is also included to provide bearing information at long range. In partnership with the Canadian Space Agency (CSA) and NASA, Neptec has space qualified the TriDAR vision system and integrated it onboard the Space Shuttle Discovery to fly as a Detailed Test Objective (DTO) on the STS-128 mission. The objective of the TriDAR DTO mission 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. Autonomous operations involve automatic acquisition of the ISS, real-time tracking as well as detection and recovery from system malfunctions and/or loss of tracking. This paper presents an overview of the TriDAR system as well as results from on-orbit testing of the TriDAR during the STS-128 Space Shuttle mission.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".