Environment mapping using probabilistic quadtree for the guidance and control of autonomous mobile robots
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents the implementation of an environment mapping technique based on a probabilistic quadtree which records the location of static and dynamic obstacles, as well as the certainty over each obstacle's estimated position. The quadtree-based map is updated online (i.e. near-real time) based on multi-sensor feeds originating from one to three X80 mobile robots operating simultaneously. The probabilistic quadtree map is part of a guidance and navigation control system which combines a Genetic Algorithm-based global path planner and a Potential Field local controller. The centralized map is shared by all mobile robots although each robot has an independent controller. Performance of the proposed method for this guidance and navigation control system is demonstrated experimentally with the Dr. Robot's ™ wireless X80 mobile robots.
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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 | 0.001 | 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