Speed control of a mobile robot using neural networks and fuzzy logic
Why this work is in the frame
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Bibliographic record
Abstract
When a certain control function is hard to achieve using a single intelligence technique, collaboration between different ones may succeed in performing such a complicated mission. This paper shows a mobile robot playing a significant role in a clean-room medical factory, where it is not recommended for the human to work. In that environment, neural networks and fuzzy logic were combined to form a suitable solution to perform the dedicated missions. In order to perform the speed control of a mobile robot, multi-layered neural networks designed for environmental recognition, and local navigation feed the fuzzy system with signals of change in direction with the nature of the sub-space of the working environment. To prove this concept, a computer based design and test of the computational intelligence system is performed. This system includes three neural controllers for local navigation, two neural networks for environmental recognition, and a fuzzy system for speed control. The system is fed off-line by a simulated model of a laser range-finder. These major components of the control system perform a global neural navigation and a fuzzy-neural speed control that guide a mobile robot to track its predefined path to arrive to its final goal through a set of sub-goals, or autonomously plan its path to arrive to the desired final goal, while avoiding obstacles that are found along the way.
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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) | 0.000 | 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