An adaptive vision system for guidance of a robotic manipulator to capture a tumbling satellite with unknown dynamics
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Bibliographic record
Abstract
This paper is focused on an adaptive vision system for the guidance of a robot to intercept a non-cooperative target satellite with unknown dynamics parameters. A Kalman filter is developed to reliably estimate the states of the object as well as all of its inertial parameters - namely, the moment-of-inertia ratios, the center-of-mass location, and the orientation of the principle axes - from vision information. The estimates are then used to optimally plan the motion of the manipulator. The optimization performance index includes the time of travel and the weighted norms of the end-effector velocity and acceleration, and it is subject to the conditions that the robot end-effector and the satellite gasping point arrive at the rendezvous point with the same velocity and that the interception occurs within the robot reach. The variational method is used to find the optimal path, which turns out to be the solution of a fourth-order differential equation. Subsequently, a closed-form solution is obtained. The solution to the optimal terminal-time problem is also obtained from the Hamiltonian of the entire system. Experiments are conducted by using a robotic arm to move a satellite mockup according to orbital mechanics and measuring the satellite pose by a laser camera system. The results demonstrate a successful grasping even though the inertial parameters are not known by the control system.
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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