Calibration of omnidirectional wheeled mobile robots: method and experiments
Bibliographic record
Abstract
SUMMARY Odometry errors, which occur during wheeled mobile robot movement, are inevitable as they originate from hard-to-avoid imperfections such as unequal wheels diameters, joints misalignment, backlash, slippage in encoder pulses, and much more. This paper extends the method, developed previously by the authors for calibration of differential mobile robots, to reduce positioning errors for the class of mobile robots having omnidirectional wheels. The method is built upon the easy to construct kinematic formulation of omnidirectional wheels, and is capable of compensating both systematic and non-systematic errors. The effectiveness of the method is experimentally investigated on a prototype three-wheeled omnidirectional mobile robot. The validations include tracking unseen trajectories, self-rotation, as well as travelling over surface irregularities. Results show that the method is very effective in improving position errors by at least 68%. Since the method is simple to implement and has no assumption on the sources of errors, it should be considered seriously as a tool for calibrating omnidirectional mobile having any number of wheels.
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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) | 0.000 | 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) | 0.001 | 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".