Autonomous Parallel Parking of a Car-Like Mobile Robot by a Neuro-Fuzzy Behavior-Based Controller
Bibliographic record
Abstract
In this paper, the concept of sensor-based behavior is used to design a neuro-fuzzy control system for a car-like-mobile-robot. The problem addressed is the parallel parking in a rectangular constrained space with just one backward maneuver. To accomplish the autonomous fuzzy behavior control, the car-like-mobile-robot has trained to park in just 2 parking dimensions based on the training data obtained from sensor information generated offline by adopting a fifth-order polynomial as the reference trajectory. The proposed controller is an ANFIS architecture that generates turning angle as output. As long as the states (positions and orientations) of the robot are measurable at each discrete-time step during the control process, this controller can make the robot follow feasible trajectories by just knowing the initial configuration of the robot and park successfully at the prescribed goal position. The simulation results which are based on real dimensions of a typical car demonstrate the feasibility and effectiveness of the proposed controller in practical car maneuvers.
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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 | 0.000 |
| 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 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".