Kinematic Design and Prototyping of a Gripper With Grasping and Scooping Capabilities Driven by the Redundant Degrees of Freedom of a Parallel Robot
Bibliographic record
Abstract
Abstract A gripper design is adapted to offer grasping and scooping capabilities to a parallel robot. This enables the parallel robot to manipulate not only large objects, but also thin objects lying on flat surfaces. Moreover, this gripper is driven directly by the redundant degrees of freedom of the parallel robot to which it is integrated. Thus, by eliminating actuators from the gripper, weight is drastically reduced, thereby making it possible to take advantage of the full payload of the parallel robot. The kinematic architecture of the gripper is first presented, notably, the kinematic implications of using an epicyclic mechanism. Then, the kinematic model developed to integrate the gripper to a (6 + 3)-degree-of-freedom robot is presented. Trajectory planning strategies for both grasping and scooping are then presented together with the parameters used. Finally, the experimental validation of these manipulation methods is discussed briefly to assess foreseeable improvements to the gripper itself as well as the trajectory planning aspect of the manipulation methods.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".