Confidence-Guided Path Planning for Mobile Sensors
Confidence-Guided Path Planning for Mobile Sensors
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DOI：
10.1109/globecom54140.2023.10437189
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发表时间：
2023-12
期刊：
影响因子：
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通讯作者：
D. Turgut;O. P. Kreidl;Ayan Dutta;Ladislau Bölöni
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文献类型：
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作者：
D. Turgut;O. P. Kreidl;Ayan Dutta;Ladislau Bölöni
本文介绍了置信度引导路径规划（CGP），规划的移动的传感器节点的路径的目标，以增加在数据收集过程中的任何时间点的估计模型的准确性的信心的算法。该方法采用基于高斯过程回归的局部估计器，并利用不确定性估计将传感器引导到置信度较低的区域。在一项实验研究中，我们比较了CGP与系统的割草机式探索和随机航点运动，我们发现，CGP在大部分的探索过程中取得了更好的成绩，只有完全完成系统的探索。我们还发现，作为追求更高置信度的一种新特性，CGP实现了对感兴趣区域的良好覆盖。该算法在精确农业、野生动物跟踪和道路监控等领域具有广泛的应用前景，但在这些领域，穷举覆盖是不可行的。
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.