Force Balancing of Robotic Mechanisms Based on Adjustment of Kinematic Parameters
Bibliographic record
Abstract
Force balancing is a very important issue in mechanism design and has only recently been introduced to the design of robotic mechanisms. In this paper, a force balancing method called adjusting kinematic parameters (AKP) for robotic mechanisms or real-time controllable (RTC) mechanisms is proposed, as opposed to force balancing methods, e.g., the counterweights (CW) method. Both the working principle of the AKP method and the design equation with which to construct a force balanced mechanism are described in detail. A particular implementation of the AKP method for the RTC mechanisms where two pivots on a link are adjustable is presented. A comparison of the two methods, namely the AKP method and the CW method, is made for two RTC mechanisms with different mass distribution. The joint forces and torques are calculated for the trajectory tracking of the RTC mechanisms. The result shows that the AKP method is consistently better than the CW method in terms of the reduction of the joint forces and the torques in the servomotors, and the smoothing of the fluctuation of the joint force.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".