Distributed Roadmaps for Robot Navigation in Sensor Networks
Why this work is in the frame
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Bibliographic record
Abstract
This paper studies a distributed path-planning problem: How can a sensor network help navigate a nontrial robot to its desired goal in a distributed manner? We consider the case where each sensor node is equipped with sophisticated sensors capable of giving a map for its sensing region. We propose a distributed sampling-based planning algorithm, where every sensor node creates a local roadmap in its locally sensed environment; these local roadmaps are “stitched” together by passing messages among nodes and forming a larger implicit roadmap without having a global representation. Based on the implicit roadmap, a feasible path is computed in a distributed manner, and the robot moves along the path by interacting with sensor nodes, each of which giving a portion of the path within the local environment of the node. Simulations show that the algorithm is able to solve the path-planning problem with low communication overhead.
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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 | 0.001 | 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