Why this work is in the frame
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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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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