Bibliographic record
Abstract
Wheeled mobile robots (WMRs) are very interesting regarding different applications from in-house activities in assisting elderly people and patients to space exploration. While the design concept and the application of the WMRs determine specifications of the robot, the positional errors occur during the WMR motion. The positional errors are inevitable, as they are caused by imperfections in design to fabrication; therefore, there is a need to rectify them using calibration techniques such as odometry, camera-based error detection, or using gyroscope and compasses. This chapter focuses on the use of odometry as it provides improved short-term accuracy with high sampling rates while it is more economical and requires fewer landmarks to localize the WMR. The context provides an overview of WMRs mechanisms, differential and omnidirectional drive, and then introduces an odometry-based method to correct the motion of both types of WMRs. Experimental results on four robots exhibited that positional error was significantly improved. Using analysis of variance (ANOVA) test, the authors could not detect any change in error improvement when the robot changed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".