Grasping Control for Kinematically Redundant Parallel Robots With a Remotely Operated Gripper
Bibliographic record
Abstract
This article proposes a novel grasping control method for a kinematically redundant parallel (KRP) robot with a remotely operated gripper. The proposed method consists of a motion controller and a force controller acting on each leg of the robot. The motion control model applies the computed torque method, and the force control model is based on the forces applied by each leg to the platform. These forces are decomposed into two main components: The first component generates the grasping forces, while the second one balances the platform. Since the different force components are calculated independently, the grasping forces are not coupled with others. As a result, the grasping forces can be precisely controlled without using any force/torque sensor. The grasping process introduces actuation redundancy and changes the topology of the KRP robot. In such a situation, there are infinitely many solutions for the forces in the robot, and three approaches are proposed to resolve the overconstraint. Once the forces applied on each leg are obtained, the actuated torques can be calculated based on the static force model of the leg. Finally, experiments are conducted on a prototype to verify the performance of the proposed control method.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".