Stabilizing NMPC of wheeled mobile robots using open-source real-time software
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Bibliographic record
Abstract
In this paper, a recently developed open-source toolkit implementing fast nonlinear model predictive control (NMPC) routines has been utilized to achieve the two main control objectives of nonholonomic mobile robots, namely, point stabilization and trajectory tracking. The stability of the controller has been guaranteed by adding final state equality constraint, which generally requires, for feasibility, long optimization horizon and hence is computationally demanding. In order to use the toolkit for real experiments, a C++ code, which couples the toolkit and a wheeled mobile robot research platform's software, has been developed. Full scale experiments have been conducted showing the applicability of the stabilizing terminal equality constraint NMPC, to wheeled mobile robots' control problems, in a real-time framework.
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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) | 0.001 | 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 it