A practical approach to control and self-localization of an omni-directional mobile robot
Bibliographic record
Abstract
Abstract:- Due to their advantages, omni-directional mobile robots have found many applications especially in robotic soccer competitions. Despite recent advances, effective control and self-localization of omni-directional mobile robots remain as important and challenging issues. In this work, a practical approach for control and self-localization of an omni-directional robot is proposed. For this purpose, a simplified model of the system is derived for fast tuning of the control system parameters. In particular, strategies for fast tuning of PID/PD coefficients for position and orientation control are devised. A vision-based self-localization and the conventional odometry systems are fused for robust self-localization. The methods have been tested in the RoboCup competition field using three Persia middle size omni-directional robots. The experimental results are shown to demonstrate the effectiveness of the proposed system and its comparison with a few other compatible approaches.
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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 | 0.000 |
| 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 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".