The SPHERES Navigation System: from Early Development to On-Orbit Testing
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
Engage and Reorient Experimental Satellites (SPHERES) facility for the testing of for-mation flight and autonomous docking algorithms inside the International Space Station (ISS), in NASA’s reduced gravity aircraft and in a 1-g laboratory environment. To pro-vide SPHERES with reliable and accurate position, velocity, attitude and angular rate estimation, an innovative state estimation system based on ultrasound transmission has been developed. An extended Kalman filter (EKF) processes time-of-flight data collected by ultrasonic receivers, as well as angular rate measurements provided by gyroscopes, to compute the state estimates required by the satellites when maneuvering. To increase the robustness of the system, the EKF has been augmented with a fault detection capability that uses the filter innovation (residual) to diagnose measurement errors. Two versions of the algorithm were successfully implemented and used on the SPHERES facility onboard the ISS during a series of five test sessions, from May 2006 to November 2006. This paper describes both versions in detail, along with difficulties encountered during the implemen-tation on the hardware and their solution. Results from experiments performed in the ISS to validate the algorithms are also presented.
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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