Synthesis of 3-[P][S] Parallel Mechanism-Inspired Multimode Dexterous Hands With Parallel Finger Structure
Bibliographic record
Abstract
Abstract Dexterous hands are an important end-effector of robots, but their relatively low carrying capacity, small workspace and poor task adaptability are the key factors that restrict their wide application. To overcome these shortcomings of dexterous hands, a novel Lie-group-based synthesis method that extends the 3-[P][S] parallel mechanisms (PMs) to dexterous hands is presented, and a class of three-finger dexterous hands with parallel finger structure is obtained. The multimode operation is proposed by designing a double-slider palm that provides the hands with a large workspace and high task adaptability. The operation types are presented, and the dexterous in-hand manipulations in all modes are analyzed by means of Lie group theory. In addition, the equivalent structural characteristics of pinching objects are classified to elucidate the motion types and the rotational properties of the pinched objects. The inverse kinematics of fingers is presented and is used to identify the input–output relationships. Finally, the workspaces of the fingers are determined according to the result of the inverse kinematics, and the relationships between the size and displacements of the pinched object are presented. The proposed dexterous hands overcome the problems of low carrying capability, small workspace, and weak in-hand manipulation ability that are encountered with the traditional dexterous hands, which are underactuated and are built with a series finger structure, and can be potentially applied to various application domains, such as services, industry, and rescue.
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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) | 0.002 | 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".