Atlas motion platform split-axle mecanum wheel design
Bibliographic record
Abstract
The Atlas motion platform was conceptually introduced in 2005 as a 2.90 m diameter thin-walled composite sphere housing a cockpit. Three active mecanum wheels provide three linearly independent torque inputs enabling the sphere to enjoy a 100% dexterous reachable workspace with unbounded rotations about any axis. Three linearly independent translations of the sphere centre, decoupled from the orientation workspace, are provided by a translational three degree-of-freedom platform. Small-scale and half-scale demonstrators introduced in 2005 and 2009, respectively, gave us the confidence needed to begin the full-scale design. Actuation and control of the Atlas full-scale design is nearing completion; however, resolution of several details have proven extremely elusive. The focus of this paper is on the design path of the 24 passive mecanum wheels. The 12 passive wheels below the equator of the sphere help distribute the static and dynamic loads, while 12 passive wheels above the equator, attached to a pneumatically actuated halo, provide sufficient downward force so that the normal force between the three active wheel contact patches and sphere surface enable effective torque transfer. This paper details the issues associated with the original twin-hub passive wheels and the resolution of those issues with the current split-axle design. Results of static and dynamic load tests are discussed.
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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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".