3-D flexible fixturing using a multi-degree of freedom gripper for robotic fixtureless assembly
Bibliographic record
Abstract
A novel grasping strategy and gripper for fixturing in 3D is presented for the robotic fixtureless assembly application. The goal of the strategy is to accurately immobilize a part in the presence of initial robot and part positioning errors. The grasping strategy expands a previously developed 2D theory into 3D and is implemented on two automotive parts using a multi-degree of freedom gripper. The gripper is able to fixture a variety of parts and the only change is reconfiguration of the computer controlled axes. To fixture a sheet metal part, the fingers are placed within holes of the part and moved until the desired set of contact locations is achieved. The fingers are grooved at fixed angles such that the edge of the sheet metal part can be held within the grooves. Three fingers and six frictionless point contacts are used for each part. A computer algorithm is described that solves for suitable contact locations based on the part geometry. The algorithm was implemented and tested on two Buick sheet metal parts from the front fender assembly. Twenty five trials were performed for each grasp. The standard deviation of the part location prior to being grasped was 0.43 mm. After being grasped, this was reduced to 0.01 mm.
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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 | 0.001 |
| 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 | 0.001 | 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".