A Versatile Design Platform for a Walking Robot in Harsh Environments
Bibliographic record
Abstract
Abstract In a previous paper [1] on the design of Capra — a quadruped robot with improved agility, we had presented the main design avenues that were to be considered for the development of an agile mobile walking robot, Capra. The objective of achieving full and autonomous robot agility in harsh environments has always been predominant in our research program on robot design for improved capabilities and applications potential. In this follow-up paper, we report the design results to meet another level of objectives that we had previously determined and currently are incorporating in the Capra hierarchy. Systematic design methodologies that were developed for the robotic machine structure also enable one to consider the Capra robot as not only an agile walking-machine but also as a versatile design platform. It can be deployed for retrofit in many automation applications or for specific uses in harsh environments involving critical design constraints. This design approach has resulted in the development and integration of many of Capra’s fundamental sub-systems by means of a range of modules and prototypes. This paper addresses all the different design issues involved in the development of a versatile design platform for a truly mobile robot. The design methodologies to support the various capabilities are presented here as independent systems, which can be fitted, integrated and retrofitted for any mobile robot configuration. Several design results are presented as examples of the design process.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".