S. Matloob, A. Dutta, P. Kreidl, D. Turgut, and L. Bölöni. Exploring the Tradeoffs Between Systematic and Random Exploration in Mobile Sensors. In Proc. of MSWiM 2023, pp. 209–2016, October-November 2023. DOI: 10.1145/3616388.3617524
The movement of a mobile sensor has a critical impact on the information gathered from the area of interest, as well as the quality of the estimate that a model can build from the collected information at any moment in time. Both systematic exploration models, which make the sensor move in regular patterns, and random movement models have specific advantages. There is less research concerning models that are positioned between these two extremes. In this paper, we propose Grid Limited Randomness (GLR), a family of path planning algorithms based on sampling waypoints from a grid of a specific resolution. We propose three variations differentiated by the order in which the mobile sensor visits these waypoints: new samples added to the end of the path (GLR-EOP), smallest detour (GLR-SD), and the shortest path as approximated by Christofides' algorithm. An extensive simulation study in the Waterberry Farms benchmark shows that the GLR variations offer benefits that, in specific circumstances, make them preferable to both fully random and fully systematic exploration paths.
@inproceedings{Matloob-2023-MSWiM,
author = "S. Matloob and A. Dutta and P. Kreidl and D. Turgut and L. B{\"o}l{\"o}ni",
title = "Exploring the Tradeoffs Between Systematic and Random Exploration in Mobile Sensors",
booktitle = "Proc. of MSWiM 2023",
pages = "209--2016",
year = "2023",
month = "October-November",
note = "DOI: 10.1145/3616388.3617524",
abstract = {The movement of a mobile sensor has a critical impact on the information gathered from the area of interest, as well as the quality of the estimate that a model can build from the collected information at any moment in time. Both systematic exploration models, which make the sensor move in regular patterns, and random movement models have specific advantages. There is less research concerning models that are positioned between these two extremes. In this paper, we propose Grid Limited Randomness (GLR), a family of path planning algorithms based on sampling waypoints from a grid of a specific resolution. We propose three variations differentiated by the order in which the mobile sensor visits these waypoints: new samples added to the end of the path (GLR-EOP), smallest detour (GLR-SD), and the shortest path as approximated by Christofides' algorithm. An extensive simulation study in the Waterberry Farms benchmark shows that the GLR variations offer benefits that, in specific circumstances, make them preferable to both fully random and fully systematic exploration paths.},
}
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