Drive-train selection criteria for <i>n</i>-dof manipulators: basis for modular serial robots library
Bibliographic record
Abstract
Abstract Towards planning a modular library for customized designs of serial manipulators, a trade-off is required between minimum modules inventory and maximum robotic applications to be handled. This paper focusses at the types of modules which are majorly based upon optimized payload capacity of the modular links. To find minimum types of modules in the modular library, an exercise has been performed on a large variety of robotic manipulators, with variations in degrees-of-freedom (dof) between 3 and 9 in number and that in payload capacity between 0 and 5 in kgs. Observing the pattern of the maximum-torque based drive-train selections for all the manipulators in consideration, three types of actuators are selected from a set of Maxon motor-gear assemblies. Subsequently, three types of modules are planned—Heavy (H), Medium (M) and Light (L). Challenge involved is the maximum load estimations for each joint involving variations due to large number of dof, various possible configurations and realistic weight estimation. This paper provides a general recursive framework for optimized drive-train, with one step as determination of maximum load estimation at a joint, and the second step as the selection of appropriate motor-gear assembly for the joint—providing an appropriate weight estimation for critical-configuration evaluation of the next link. The methodology is utilized for planning optimized number of modular divisions, for evaluating payload capacity of each division and possible modular combinations for given number of degrees-of-freedom.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".