Bibliographic record
Abstract
In this paper, the trajectory-planning problem of a mobile robot is studied with an application to near optimal energy parking. A hybrid data-driven neuro-fuzzy system composed of two steps is developed; first, we introduce a preprocessing step involving offline trajectory parking, and generating reference optimal energy trajectories, while satisfying several constraints related to robot kinematics and dynamics and parking lot limitations. The discrete augmented Lagrangean is implemented to solve the resulting non-linear and non-convex optimal control problem. The outcomes of this pre-processing step allow building a neuro-fuzzy inference system to learn and capture the robot multi-objective dynamic behavior. The second step is a sensor-based neuro-fuzzy navigation scheme. From the learnt optimal energy behavior dataset, a 6-input/2-output ANFIS network is built for online parking. This network considers the three range measurements obtained from three sonar sensors mounted at 3 directions at the front left corner of the robot. In addition, the discrepancy between the current measured distance and the previous measured one, has been implemented to generate a control output consisting of the robot motor torques. First results based on real dimensions of a typical car, demonstrate the 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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".