D. Turgut, P. Kreidl, A. Dutta, and L. Bölöni

Confidence-guided path planning for mobile sensors


Cite as:

D. Turgut, P. Kreidl, A. Dutta, and L. Bölöni. Confidence-guided path planning for mobile sensors. In Accepted to appear at IEEE GLOBECOM 2023, December 2023.

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Abstract:

This paper introduces Confidence Guided Path-planning (CGP), an algorithm for planning the path of mobile sensor nodes with the goal to increase confidence in the accuracy of the estimated model at any time point in the data collection process. The approach employs a local estimator based on a Gaussian process regressor and takes advantage of the uncertainty estimation to guide the sensor to areas of lower confidence. In an experimental study comparing CGP with systematic lawnmower-type exploration and random waypoint movement, we found that CGP achieves better scores than both during most of the exploration process, being outperformed only by a fully completed systematic exploration. We also found that, as an emergent property of pursuing higher confidence, CGP achieves good coverage of the area of interest. The proposed algorithm has wide applications in precision agriculture, wildlife tracking, and road monitoring, where exhaustive coverage is not feasible.

BibTeX:

@inproceedings{Turgut-2023-GLOBECOM,
	author = "D. Turgut and P. Kreidl and A. Dutta and L. B{\"o}l{\"o}ni",
	title = "Confidence-guided path planning for mobile sensors",
	booktitle = "Accepted to appear at IEEE GLOBECOM 2023",
	year = "2023",
	month = "December",
	abstract = {This paper introduces Confidence Guided Path-planning (CGP), an algorithm for planning the path of mobile sensor nodes with the goal to increase confidence in the accuracy of the estimated model at any time point in the data collection process. The approach employs a local estimator based on a Gaussian process regressor and takes advantage of the uncertainty estimation to guide the sensor to areas of lower confidence. In an experimental study comparing CGP with systematic lawnmower-type exploration and random waypoint movement, we found that CGP achieves better scores than both during most of the exploration process, being outperformed only by a fully completed systematic exploration. We also found that, as an emergent property of pursuing higher confidence, CGP achieves good coverage of the area of interest. The proposed algorithm has wide applications in precision agriculture, wildlife tracking, and road monitoring, where exhaustive coverage is not feasible.},
}

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