Decentralized Kinematic Control of a Cooperating System of Mobile Manipulators
Bibliographic record
Abstract
In this paper, we examine the development of a decentralized control framework for a modular system of 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, planar, revolute-jointed arm mounted in the plane parallel to the base of the WMR. 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 accommodate relative positioning errors of the mobile bases as well as change its relative configuration. Particular attention is paid for the development of kinematic control schemes for mobile manipulators, which take into account the non-holonomic constraints of the base and the presence of passive joints in the manipulator system. The control scheme developed for the individual mobile manipulators is then adapted for the decentralized kinematic control of two mobile manipulators carrying a common object along a desired trajectory. Experimental evaluation of the performance of the resulting approach and the ability of the overall collaborating system to accommodate, detect and correct for relative positioning errors between the mobile platforms is also presented.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 0.001 |
| 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".