Rapid Prototyping for Robotics
Bibliographic record
Abstract
The rapid prototyping framework presented in this chapter provides fast, simple and inexpensivemethods for the design and fabrication of prototypes of robotic mechanisms.As evidenced by the examples presented above, the prototypes can be of great help togain more insight into the functionality of the mechanisms, as well as to convey theconcepts to others, especially to non-technical people. Furthermore, physical prototypescan be used to validate geometric and kinematic properties such as mechanicalinterferences, transmission characteristics, singularities and workspace. Actuated prototypes have also been successfully built and controlled. Actuated mechanisms can be used in lightweight applications or for demonstration purposes. The main limitation in such cases is the compliance and limited strength of the plasticparts, which limits the forces and torques that can be produced. Finally, several comprehensive examples have been given to illustrate how the rapidprototyping framework presented here can be used throughout the design process. Two robotic hands and a SLA machine model demonstrate a wide variety of link and jointfabrication methods, as well as the possibility of embedding sensors and actuators directlyinto mechanisms. In these examples, rapid prototyping has been used to demonstrate,validate, experimentally test (including destructive tests), modify, redesign and,in one case, support the machining of a metal prototype. 43
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.077 | 0.060 |
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".