Optimal Configuration Selection for a Cooperating System of Mobile Manipulators
Bibliographic record
Abstract
In this paper, we examine the design and selection of optimal configuration of a system of multiple, wheeled mobile manipulators that can team up to cooperatively transport a large common object. Each individually autonomous mobile manipulator consists of a differentially driven wheeled mobile robot (WMR) with a passive, two degree-of-freedom (d.o.f), planar revolute-jointed arm mounted in the plane parallel to the base. The composite multi-degree-of-freedom vehicle, formed by placing a common object on the end-effector of two (or more) such mobile manipulator systems, possesses the ability to change its relative configuration as well as accommodate relative positioning errors of the mobile bases. However, the combination of the non-holonomic constraints of the wheeled mobile bases, the presence of passive joints and closed kinematic loops of the articulations require a careful treatment. The suitable selection of the dimensions of the intermediate linkage and the overall configuration is critical to enhancing the overall performance. In this paper we will focus our attention on the formulation of a design-optimization problem to aid the selection of the configuration of the composite vehicle to attain near-isotropic manipulability of the common object.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".