Visual-Servo Autonomous Robotic Manipulators for Capturing Non-Cooperative Target
Bibliographic record
Abstract
This paper presents a methodology of vision-based pose and motion estimation of non-cooperative targets as well as a control scheme for robotic manipulators to perform autonomous capture of non-cooperative targets. A combination of photogrammetry and extended Kalman filter is proposed for real time state estimation of the non-cooperative target. Once the vision-based estimation is obtained, a real state of the target regarding to the global frame is calculated based on the transformation matrices of coordinate frames. So as to make a capture, a desired state of the end effector is defined in accordance with the real state of the target aforementioned, and further a corresponding desired state of the robotic manipulator is derived by inverse kinematics. Then a close-loop control scheme is adopted to drive the robot to the desired state previously obtained. Experiments have been designed and implemented on a custom built six degrees of freedom robotic manipulator with an eye-in-hand configuration. The experimental results demonstrated the feasibility and effectiveness of the proposed methodology and control scheme.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".