Analysis and design of an embedded fuzzy motion controller for a behavior-based nonholonomic mobile robot
Bibliographic record
Abstract
In this study, a fully autonomous mobile robot is built successfully by using the behavior-based artificial intelligence approach. Several levels of competences and behaviors are implemented. Each module itself in the developed mobile robot generates behaviors, and the improvement in the competence of the system proceeds by adding new modules to the system. Analysis and design of fuzzy control laws for steering control of the autonomous nonholonomic mobile robot are presented. The approach uses Lyapunov's direct method to formulate a class of control laws that guarantee the convergence of the steering error. Certain requirements for the control laws are presented for the designers to choose a suitable rule base for the fuzzy controller in order to make the system asymptotically stable. The stability of the proposed fuzzy controller is approached theoretically and also demonstrated by simulation studies. Simulations using the model of a four degree-of-freedom nonholonomic mobile robot are conducted to investigate the performance of the proposed fuzzy controller. The mobile robot can achieve the desired turn angle and follow the target satisfactorily.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".