Multi-axis Reorientation of a Free-falling Omnidirectional Wheeled Robot
Bibliographic record
Abstract
This paper presents reorientation manoeuvres applied to an omnidirectional wheeled robot for impact mitigation during short falls. The proposed robot architecture aims to build upon recent innovations in reorientation robots to attain fast, multi-axis reorientation. Indeed, the use of omnidirectional wheels allows for simplifications to be made with respect to previous mobile robot architectures that make the proposed architecture more efficient for free fall reorientation, while still maintaining free roaming capabilities. To test these improvements, a prototype is built and a free roam and two free fall demonstrations are completed. On the one hand, the free roam demonstration validates that translation along both horizontal axes and rotation about the yaw axis are achieved with the presented prototype. On the other hand, the first free fall demonstration shows that a worst case scenario of a 180-degree reorientation about one axis can be completed in just under 0.45 seconds (one-metre fall) and the second free fall demonstration validates that the prototype is capable of simultaneous reorientation about both the roll and pitch axes. Therefore, the fast, multi-axis reorientation capabilities of the developed prototype are verified.
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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 | 0.001 |
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".