Bibliographic record
Abstract
Building a robotic platform from raw materials can take anything from a few weeks to a few years to complete depending on the complexity and size of the platform. We introduce a novel approach of using a water jet cutter for manufacturing a robot vehicle body within days. Both light weight and strong materials like aluminium and different engineering plastics can be cut to create prototype chassis/ bodies or even the final product. These platforms are quite ruggedized and can be used in varying environments for different applications. The two platforms manufactured using this approach are used as a mine inspection robot (Shongololo) and the other as an all-terrain vehicle to be used in vineyards(Dassie). “Shongololo’s” frame is made from engineering plastics and the chassis of “Dassie” was made from aluminium and cut using the water jet cutter. The advantage of using the water jet cutter is the speed at which the final product can be achieved and in addition little or no extra finishing is needed. The chassis were made up of different sheets with internal shapes cut out to reduce the weight of the robot. Most mounting and screw holes can also be cut into the sheets. This approach simplifies the manufacturing process for prototyping robotic platforms. Using the water jet cutter approach allows us to build medium size robotic platforms with varying complexity in a matter of days compared to weeks using traditional machining methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".