A multi-agent system based on active vision and ultrasounds applied to fuzzy behavior based navigation
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we present a multi-agent system that uses both visual and range sensors information to achieve a safe and efficient behavior-based navigation. The system uses a topological map of an indoor office-like environment and it is based on fuzzy behaviors, providing to the robot the ability to find doors in rooms. The system is formed by distributed agents that can establish communication among themselves. We use both reactive and deliberative agents and we have carried out a modular design of the system to facilitate its posterior expansion by adding new skills or new agents. Also, the use of a multi-agent system allows us to achieve a more robust performance of the robot. Regarding the behaviors, they have been designed using fuzzy rules to set the appropriate relationship between the input data and the control values to apply to the robot actuators. The system has been implemented in a Nomad 200 mobile robot and has been validated in numerous tests in a read office-like environment
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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