A behavior-based mobile robot with a visual landmark-recognition system
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, based on behavior-based artificial intelligence we have built a fully autonomous mobile robot. Several modules are developed for the mobile robot to implement different levels of competences and behaviors, where each module itself generates behaviors. New modules can be easily added to the robot system to improve in the competence without changing any existing modules. A vision-based landmark recognition system for robot navigation is developed as the highest layer in the subsumption architecture. A genetic-algorithm-based search method for pattern recognition of digital images is proposed and implemented to recognize artificial landmarks by searching all the predefined patterns. The vision layer is capable of generating the desired behaviors corresponding to various landmarks. A combination of eight ultrasonic sensors is designed to implement obstacle-avoidance behaviors through a set of fuzzy rules. The effectiveness of this behavior-based mobile robot is demonstrated by experimental studies.
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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 | 0.001 |
| 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