Calibration of wheeled mobile robots with differential drive mechanisms: an experimental approach
Bibliographic record
Abstract
SUMMARY Exact knowledge of the position and proper calibration of robots that move by wheels form an important foundation in mobile robot applications. In this context, a variety of sensory systems and techniques have been developed for accurate positioning of differential drive mobile robots. This paper, first, provides a brief overview of mobile robots positioning techniques and then, presents a new benchmark method capable of calibrating mobile robots with differential drive mechanisms to correct systematic errors. The proposed method is compared with the commonly used University of Michigan Benchmark (UMBmark) odometry method. Two sets of comparisons are conducted on six prototyped robots with differential drives. The first set of tests establishes the workability and accuracy that can be achieved with the new method and compares them with the ones obtained from the UMBmark technique. The second experiment compares the performance of a mobile robot, calibrated with either the UMBmark or the new method, for an unseen path. It is demonstrated that the proposed method of calibration is simple to implement, and leads to accuracy comparable to the UMBmark method. Specifically, while the error corrections in both methods are within ±5% of each other, the proposed method requires single straight line motion for calibration, which is believed to be simpler and less timely to implement than the square path motion required by the UMBmark technique. The method should therefore be considered seriously as a new tool when calibrating differential drive mobile robots.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".