Static Model-Based Grasping Force Control of Parallel Grasping Robots With Partial Cartesian Force Measurement
Bibliographic record
Abstract
This article presents a static model-based approach for grasping force control of a class of low-inertia kinematically redundant parallel and hybrid parallel robots with remotely operated gripper. Although these robots are originally studied with kinematic redundancy, they can also be considered to be redundantly actuated or nonredundant depending on the interactions with the environment. Three different static models are then developed according to the types of redundancy and velocity Jacobians, which are demonstrated through a planar and a spatial parallel robots. The possibility of implementing these models for grasping force control is analyzed. Moreover, we show that the Cartesian force and moment included in the closed force control loop do not need to be measured directly by multidegree-of-freedom force/torque sensors, but can be calculated based on the grasping force that is acting on each jaw of the gripper, which is easily measured using a load cell. A combined position and grasping force control scheme is proposed and experiments are conducted to verify the effectiveness of the nonredundant static model. The proposed approach is readily applied to other kinematically redundant parallel grasping robots.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".