Integrating an electronic compass for position tracking on a wheeled tricycle mobile robot
Why this work is in the frame
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Bibliographic record
Abstract
Dead-reckoning via encoders on wheeled-mobile robots is a simple but inaccurate method to estimate position. The major drawback of encoders is wheel slippage errors that accumulate over time. This problem is often addressed by using additional sensors such as compass, gyroscope, or GPS. This paper details the integration and effectiveness of a relatively low-cost solution using an electronic compass to reduce positioning error on a wheeled tricycle mobile robot. A customised Visual Studio program has been developed to adjust the settings of the electronic compass and integrate it with the Visual Studio based robot control system. The electronic compass heading data is fused with the encoder odometry heading data in three different ways: simple fusion, linear weighted fusion, and Kalman filter fusion. Simple fusion and linear weighted fusion rely on parameters determined from angular acceleration and angular velocity, respectively. The Kalman filter uses variance data for the encoders and electronic compass to determine an optimal heading. Experiments have been conducted in an indoor corridor environment to evaluate and compare the various fusion methods. Position error is successfully reduced and is sufficient to locate the robot within the corridor.
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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 | 0.001 | 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