Joint velocity compensation for kinematically redundant manipulators
Bibliographic record
Abstract
This paper concerns an approach for the inverse kinematic resolution of kinematically redundant manipulators when following a desired path. It can be called joint velocity compensation (JVC). Its principle is employing the redundancy to ensure uniform velocity or velocities with desired simple patterns for certain selected joints, while the remaining joints move with variable velocities as required by a given task. As many joints as the order of redundancy can have predefined velocities. From a control point of view it would be easier and more precise to maintain a uniform velocity for a joint rather than one that varies. Compared with other techniques, this approach has the advantages that it conserves the cyclic behaviour of a manipulator in the joint space for a closed path in the task space, less arithmetic operations are involved and the control of the manipulator as a whole becomes easier. Because of the physical and task constraints the choice of these selected velocities are not arbitrary. This paper elaborates the matter on how a desired velocity can be assigned to a joint such that all the constraints are respected and the task is performed as desired. Simulation results of the implementation of this approach to a redundant robot arm with 7-DOF are presented.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".