Calibration of omnidirectional wheeled mobile robots: method and experiments
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it