Adaptive Longitudinal Slip Compensation for Wheeled Mobile Robots Using Velocity Ratio
Why this work is in the frame
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Bibliographic record
Abstract
The wheel slip can lead to tremendous challenges in mobile robot performance, including difficulties in tracking a desired motion trajectory, a long time to transverse to a distance due to losing velocity, and inefficient energy utilization. This paper proposes an adaptive longitudinal slip compensation framework based on the robot’s velocity ratio information. The basic idea underlying this framework is that adaptive update law algorithms keep tracking to estimate the unknown velocity ratios to identify the presence of wheel slip. Consequently, the estimated velocity ratios can lead to an estimation of the longitudinal slip, and then adaptive control starts acting to compensate for the wheel slip. Knowledge of the robot’s kinematic model allows for deriving the velocity ratios used to determine the wheel slip. Based on the Lyapunov stability analysis, the stability of the designed closed-loop system is investigated, and the convergence of the state vector trajectories is proven using the Lyapunov-like Barbalats lemma. Finally, the analytical results are validated with simulations to demonstrate the effectiveness of the proposed scheme for trajectory tracking performance in the presence of wheel slip.
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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