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Volume 19
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10.3390/s19051016
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Article

Robotic Active Information Gathering for Spatial Field Reconstruction with Rapidly-Exploring Random Trees and Online Learning of Gaussian Processes †

by
Alberto Viseras
1,*,
Dmitriy Shutin
1 and
Luis Merino
2
1
German Aerospace Centre (DLR), Oberpfaffenhofen, 82234 Weßling, Germany
2
School of Engineering, Universidad Pablo de Olavide (UPO), 41013 Seville, Spain
*
Author to whom correspondence should be addressed.
†
This paper is an extended version of our paper published in Viseras, A.; Shutin, D.; Merino, L. Online information gathering using sampling-based planners and GPs: An information theoretic approach. In Proceedings of the 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, BC, Canada, 24–28 September 2017.
Sensors 2019, 19(5), 1016; https://doi.org/10.3390/s19051016
Received: 28 November 2018 / Revised: 19 February 2019 / Accepted: 20 February 2019 / Published: 27 February 2019
(This article belongs to the Section Intelligent Sensors)
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Abstract

Information gathering (IG) algorithms aim to intelligently select a mobile sensor actions required to efficiently obtain an accurate reconstruction of a physical process, such as an occupancy map, or a magnetic field. Many recent works have proposed algorithms for IG that employ Gaussian processes (GPs) as underlying model of the process. However, most algorithms discretize the state space, which makes them computationally intractable for robotic systems with complex dynamics. Moreover, they are not suited for online information gathering tasks as they assume prior knowledge about GP parameters. This paper presents a novel approach that tackles the two aforementioned issues. Specifically, our approach includes two intertwined steps: (i) a Rapidly-Exploring Random Tree (RRT) search that allows a robot to identify unvisited locations, and to learn the GP parameters, and (ii) an RRT*-based informative path planning that guides the robot towards those locations by maximizing the information gathered while minimizing path cost. The combination of the two steps allows an online realization of the algorithm, while eliminating the need for discretization. We demonstrate that our proposed algorithm outperforms state-of-the-art both in simulations, and in a lab experiment in which a ground-based robot explores the magnetic field intensity within an indoor environment populated with obstacles. View Full-Text
Keywords: robotics; information gathering; Gaussian processes (GPs); rapidly exploring random trees (RRT) robotics; information gathering; Gaussian processes (GPs); rapidly exploring random trees (RRT)
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MDPI and ACS Style

Viseras, A.; Shutin, D.; Merino, L. Robotic Active Information Gathering for Spatial Field Reconstruction with Rapidly-Exploring Random Trees and Online Learning of Gaussian Processes. Sensors 2019, 19, 1016. https://doi.org/10.3390/s19051016

AMA Style

Viseras A, Shutin D, Merino L. Robotic Active Information Gathering for Spatial Field Reconstruction with Rapidly-Exploring Random Trees and Online Learning of Gaussian Processes. Sensors. 2019; 19(5):1016. https://doi.org/10.3390/s19051016

Chicago/Turabian Style

Viseras, Alberto, Dmitriy Shutin, and Luis Merino. 2019. "Robotic Active Information Gathering for Spatial Field Reconstruction with Rapidly-Exploring Random Trees and Online Learning of Gaussian Processes" Sensors 19, no. 5: 1016. https://doi.org/10.3390/s19051016

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