Augmented Image Based Visual Servoing Using Image Moment Features
Bibliographic record
Abstract
In this paper, an augmented image based visual servoing (AIBVS) using image moment features is proposed to improve the visual servoing performance. The AIBVS controller produces acceleration as the controlling command. In order to generate the acceleration command, a general analytical formulation for calculating interaction matrix relating the image moments features to camera acceleration is derived. A proportional derivative (PD) controller is developed to provide the controlling command of the robot. Using the derived interaction matrix this controller can achieve smoother feature trajectory in image space and reduce the amount of overshoot that appears in the response of the system. The developed control method also enhances the camera trajectory in 3D space. Simulation tests on three image moments sets, proposed by Chaumette [1], Tahri et al. [2] and Liu et al. [3], are performed on a 6 DOFs robotic system to validate the effectiveness of the proposed controller.Copyright © 2014 by ASME
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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".