Accuracy enhancement of industrial robots by on-line pose correction
Bibliographic record
Abstract
This paper presents a novel, cost-effective dynamic pose correction (DPC) strategy to address the issues on the accuracy enhancement of industrial robots. This strategy, also known as visual-servoing, uses a photogrammetry based 6D measurement device to track the position and orientation of the robot's end-effector in real-time. To realize this strategy, we first propose a root mean square (RMS) method to filter the noise from the pose measurements. The estimated pose from the sensor serves as a feedback for visual-servoing system. Next, a DPC controller is designed and integrated with a FANUC robot controller through FANUC's dynamic path modification (DPM) software package. As a result, the robot is guided to the desired pose in real-time and hence the positioning accuracy is enhanced. Extensive experimental tests of the proposed algorithm have been carried out. The experimental results demonstrate that the pose accuracy of the robot (a FANUC M-20iA) for stationary tasks has been improved to 0.050 mm and 0.050° for position and orientation respectively.
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How this classification was reachedexpand
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 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".