Slip ratio estimation and control of wheeled mobile robot on different terrains
Bibliographic record
Abstract
This paper presents a model-based algorithm for estimating the longitudinal velocity and online slip ratio control of wheeled mobile robots (WMR). The adaptive unscented Kalman filter (AUKF) is employed to estimate the vehicle longitudinal velocity and the wheel angular velocity in the presence of parameter variations and disturbances using measurements from wheel encoders. An adaptive adjustment of the noise covariances is implemented using a covariance matching technique in the un-scented Kalman filter context for the estimation process. The loss of velocity due to the wheel slip causes extra power consumption. Due to the presence of model uncertainties, parameter variations, and disturbances in the robot nonlinear dynamic system, a sliding mode controller is designed for desired slip control. Experiments are carried out to verify the effectiveness of the estimation algorithm and the controller. In spite of uncertainties presented in the measurements, the robot/wheel dynamics, and terrain condition variations, the controller is able to provide the desired slip ratio control of the mobile robot. It is also demonstrated that the adaptive concept of AUKF leads to better results than the unscented Kalman filter in the robot states estimation which is difficult to measure in practice.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".