EVALUATION OF MOTION MAPPINGS FROM A HAPTIC DEVICE TO AN INDUSTRIAL ROBOT FOR EFFECTIVE MASTER–SLAVE MANIPULATION
Bibliographic record
Abstract
Master–slave systems with identical master and slave arms have been in vogue for many years now. With the advent of computerassisted master–slave manipulation technology, it is now convenient to use a small haptic device as a master, while a standard industrial robot serves as a slave. However, the challenge in this case is to select a suitable motion mapping from the haptic master to the slave robot. The problem is not trivial, as their degrees of freedom, workspace and inertia are all widely different. Various kinds of motion mapping have been suggested in the literature for a haptic master. We have devised a new mapping called boundary drift control. We present here the result of an experimental evaluation of the effectiveness of some of these mappings, including the one devised by us. A series of tests were conducted with a select group of operators to evaluate and compare the mappings in terms of efficiency and accuracy. Statistical significance of the observed data is established through ANOVA analysis. The role of skill, if any, in this evaluation is explored through another set of experiments. Qualitative feedbacks from the operators about ease of use of these mappings are also recorded. This study provides an insight into how to select a suitable motion mapping for a haptic master for a given job. We found that boundary drift control is well suited when both speed and accuracy are on demand.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".