Pose optimization and path improvement in robotic drilling through minimization of joint reversals
Bibliographic record
Abstract
Industrial robots have been increasingly adopted in precision manufacturing applications such as aerospace drilling. However, achieving the strict tolerance requirements of the aerospace industry has been a major challenge due to the relatively poor accuracy of robots. One of the major sources of error which has a detrimental effect on the quality and circularity of drilled holes is the static friction in robot joints. These errors are particularly pronounced when one or more joints reverse direction. To improve robot motion for better hole quality, this paper proposes an optimization framework to eliminate or minimize joint reversals throughout a drilling motion. A general robotic drilling motion with a redundant degree of freedom due to the twist of the tool is first modeled. Particle Swarm Optimization (PSO) is then used for strategic pose selection considering the entire drilling motion. Experimental tests performed on a KUKA KR 6 R700-2 show a 40% reduction in the tool deviation envelope. The proposed technique can be readily implemented on any commercial robotic drilling cell without interfering with the controller.
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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 | 0.000 | 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".