Design of a Navigation System for Robotic Free Flyers Performing Inspection of Large Space Structures
Bibliographic record
Abstract
<p>Inspection of large space structures is imperative for long term mission success. One solution is to utilize a second free flying spacecraft capable of performing inspection in orbit. An Extended Kalman Filter (EKF) is used to perform estimations on the relative position, velocity, angular velocity, and attitude through the use of navigation markers. Simulation takes place using MATLAB to compare the true values with the estimated values using the EKF. The initial covariance (P), process noise covariance (Q), and measurement noise covariance (R) matrices were tuned for a space structure with three navigation points. The largest recorded errors over 100 iterations occurred during the initial estimation yielding 26.78 centimeters in relative position, 1.80 centimeters per second in relative velocity, 0.0444 radians per second in relative angular velocity, and a difference of 0.0172 in the unit vector of relative attitude. After allowing 20 seconds of settling time the maximum errors were reduced to 5.0 centimeters in relative position, 0.40 centimeters per second in relative velocity, 0.0046 radians per second in relative angular velocity, and a difference of 0.0020 in the unit vector of relative attitude. The paper also discusses the application of training algorithms to tune the EKF parameters for future consideration.</p>
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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".