DESIGN FOR HIGH DYNAMIC PERFORMANCE ROBOT BASED ON DYNAMICALLY COUPLED DRIVING AND JOINT STOPS
Bibliographic record
Abstract
A new design approach is proposed for high dynamic performance robots, such as robots performing high-speed dynamic motions. This method is based on the utilization of dynamically coupled driving and joint stops. In the method, the dynamic performance index (DPI) formulated by the desired maximum motion specifications and the boundary conditions on initial/final configurations are combined to form a design index (DI) first. Then a dexterous mechanism consisting of very light actuators and links is initially designed under an assumption of utilizing dynamically coupled driving and joint stops. By increasing the load capabilities of the actuators step by step, an iterative process of searching for the solution of DI=0 with minimal torque needs is implemented to validate and improve the initial design. This process yields a robot that is lighter than conventional robots and capable of performing dynamic motions more efficiently by utilizing dynamically coupled driving and joint stops. Based on the method, a two-link golf swing robot performing high-speed swings is designed. Simulation results indicate the method can reduce the needs for the torque and power as compared with conventional design methods. The experiment clearly illustrates the merits of the method.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".