Development of a Robot with an Intelligent Capability to Keep and Select a Path
Bibliographic record
Abstract
The present paper is devoted to the development of a mobile robot, which is capable of following a pre-defined track and avoid collision with obstacles ahead of it. The avoidance of collision is implemented using a series of calculations and intelligent decision making support. The decisions are made according to the feedback given by the different sensors on the mobile robot, and a multilevel knowledge base programmed in the robot's controller. The robot accommodates first order logic and meta-production rules for as knowledge representation models to describe the robot's knowledge base, which defines the robot's behavior. The use of meta-production rules allows the robot's knowledge base to be fairly simple, but also flexible and extensible. Although the current intelligent capabilities of the robot are limited by avoidance of a single obstacle, the robot's knowledge base can be extended by adding new semantic and meta-production rules, that would result extensions of the robot's intelligence by new capabilities to avoid more complicated and especially multiple obstacles
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".