3-D flexible fixturing using a multi-degree of freedom gripper for robotic fixtureless assembly
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Open science | 0.000 | 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 it