Informative Path Planning for Scalar Dynamic Reconstruction Using Coregionalized Gaussian Processes and a Spatiotemporal Kernel

The proliferation of unmanned vehicles offers many opportunities for solving environmental sampling tasks with applications in resource monitoring and precision agriculture. Informative path planning (IPP) includes a family of methods which offer improvements over traditional surveying techniques fo...

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Bibliographic Details
Published in2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) pp. 8112 - 8119
Main Authors Booth, Lorenzo, Carpin, Stefano
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2023
Subjects
Gaussian processes
Kernel
Multi-robot systems
Path planning
Planning
Robot sensing systems
Spatiotemporal phenomena
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Summary:The proliferation of unmanned vehicles offers many opportunities for solving environmental sampling tasks with applications in resource monitoring and precision agriculture. Informative path planning (IPP) includes a family of methods which offer improvements over traditional surveying techniques for suggesting locations for observation collection. In this work, we present a novel solution to the IPP problem by using a coregionalized Gaussian processes to estimate a dynamic scalar field that varies in space and time. Our method improves previous approaches by using a composite kernel accounting for spatiotemporal correlations and at the same time, can be readily incorporated in existing IPP algorithms. Through extensive simulations, we show that our novel modeling approach leads to more accurate estimations when compared with formerly proposed methods that do not account for the temporal dimension.
ISSN:2153-0866
DOI:10.1109/IROS55552.2023.10341858