Extending the Force Balancing Method of Adjusting Kinematic Parameter to Spatial Mechanisms
Bibliographic record
Abstract
Abstract Robotic mechanisms is one that the mechanism includes no less than one variable speed actuator. As for force balancing, which refers to mechanisms with a complete cancelation of the inertia-induced force on the ground (shaking force). A few strategies are accessible for reducing shaking force, including the counterweight (CW), add-of-spring (AOS), add-of-linkage (AOL), and adjusting kinematic parameter (AKP). AKP is only applicable to planar robotic mechanisms when it was developed. The primary goal of the present paper is to extend AKP approach to a spatial mechanism. Considering the complex kinematic characteristics of spatial mechanism, a simple spherical parallel robot (SPR) is used as the research tool. The equations for force balancing of the spherical robotic mechanism using AKP were derived. Simulation verification was performed by a reliable software called SPACAR available in the Matlab environment. The results demonstrated the effectiveness of the AKP method in eliminating the unbalanced force to spatial mechanisms. The main contribution is the provision of a new force balancing method to spatial mechanisms, which will enrich the field of balancing of spatial robotic mechanisms.
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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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".