Adaptive Path Following for a Differential Drive Robot with EKF-based Localization
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Bibliographic record
Abstract
For a mobile robot, uncertainties in its operating environment and system setup are common challenges. The robot may need to follow a new path in order to avoid collision with a previously unknown object in the environment. Additionally, the robot does not usually have precise knowledge of its own pose. To compensate for these uncertainties, we develop an adaptive path following scheme for a differential drive robot with localization capabilities. We employ a least-squares adaptive law to estimate an unknown circular path. With measurements to landmarks in the environment, we use an extended Kalman filter to estimate the robot's pose. Furthermore, a cascaded control scheme is used to track the estimated path. Simulation and experimental results are provided to verify the developed adaptive path following scheme and demonstrate its capabilities.
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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