Kinematic Optimization of a Robotic Joint With Continuously Variable Transmission Ratio
Bibliographic record
Abstract
The range of possible tasks achievable by robots highly depends on the selection of motors and transmissions. For example, variable ratio transmissions surpass single ratio transmissions because they can modify the torque-speed parameters of the actuator and, therefore, maintain the optimal power output state from the motor. Consequently, the use of variable ratio transmissions may expand a robot’s achievable tasks. A robotic joint with continuously variable transmission ratio is presented in this paper. This new type of transmission joint may be used in serial or parallel robots. The transmission consists of a doubly actuated two-degree-of-freedom five-bar parallel mechanism. The main actuator is located at the input revolute joint. The variation of the ratio is achieved with the adjustment actuator located at a second revolute joint. Such a transmission, based on a linkage, may have unde-sired ratio variations for a constant adjustement joint position. Therefore, two different optimization methods are presented to determine the best geometric parameters in order to minimize the undesired ratio variation while maximizing the possible transmission ratio range. The performance indices are either optimal for the entire range or only for the maximum and minimum ratios available. A simulation is presented with the best parameters obtained with the optimization based on the maximum and minimum ratios. Results show a transmission ratio ranging from 0.9:1 to 4.5:1 with a minimal amplification of 3.9:1. The transmission ratio may vary continuously within the working boundaries. The output range of motion may be adapted to a serial robot joint.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".