Comparison of Basic Visual Servoing Methods
Bibliographic record
Abstract
In this paper, comparison of the position-based and image-based robot visual servoing methods is investigated, with an emphasis on the system stability, robustness, sensitivity, and dynamic performance in the Cartesian and image spaces. A common comparison framework using both predefined and taught references is defined in the context of the sensory task space robot control approach. The camera, target, and robot modeling errors in the system are considered in the comparison. Both methods are shown to be locally asymptotically stable and locally robust with respect to modeling errors. While the two methods are shown to be comparable and sensitive to the camera and target modeling errors when using predefined references, they are insensitive to these errors when using taught references. However, the system Cartesian and image trajectories and time-to-converge are affected by the camera, target, and robot modeling errors regardless of the type of references. Finally, other fundamental characteristics of the two methods including sensory task space singularity and local minima, motion coupling, and implementation issues are also compared. The comparison results are verified in simulations.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".