Sensor deployment by a robot in an unknown orthogonal region: Achieving full coverage
Why this work is in the frame
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Bibliographic record
Abstract
When deploying a wireless sensor network in an unknown environment, commonly referred to as Region of Interest (ROI), the main goal is for the entire region to be covered by the sensing ranges of the deployed sensors. While this goal of full coverage is easily achieved in presence of human intervention, it becomes problematic if the region is dangerous or inaccessible to human. An approach recently proposed to solve the problem is to use a robot to deploy the sensors; the main advantages respect to the alternative of employing mobile sensors are the reduced costs (due to manufacture and maintenance cost of common static sensors vs. mobile ones) and the reduced complexity of the coordination and control algorithms. Indeed several solution algorithms to achieve deployment of sensors by a robot in an unknown region have been proposed in the literature. Unfortunately, even when restricted to orthogonal regions (e.g., city maps, building plans, etc), all the existing algorithms fail to achieve full coverage of the ROI. Specifically, following the existing protocols, the robot would leave uncovered areas near either the boundaries or critical areas (e.g. areas that are linked to the rest of the region by a narrow corridor). In this paper we present an algorithm that overcomes these problems and guarantees that the deployment of the sensors by the robot achieves full coverage in any simply connected orthogonal ROI, whose topology is unknown to the robot. The proposed algorithm has minimal requirements: it does not need GPS but only local orientation by the robot; the communication range of a deployed sensor is limited to its deployed neighbours, and the robot has a similar range; the total number of sensors used is minimal. Also minimal are the robot's memory requirements, the total amount of robots movements and of communication between robot and sensors.
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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 | 0.001 |
| 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