Speed control of a mobile robot using neural networks and fuzzy logic
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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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 | 0.000 | 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".