Next Article in Journal
INBED: A Highly Specialized System for Bed-Exit-Detection and Fall Prevention on a Geriatric Ward
Previous Article in Journal
Three-Dimensional Electromagnetic Torso Scanner
Article Menu
Issue 5 (March-1) cover image

Export Article

Open AccessArticle
Sensors 2019, 19(5), 1016; https://doi.org/10.3390/s19051016

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

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.
Received: 28 November 2018 / Revised: 19 February 2019 / Accepted: 20 February 2019 / Published: 27 February 2019
(This article belongs to the Section Intelligent Sensors)
Full-Text   |   PDF [3459 KB, uploaded 28 February 2019]   |  
  |   Review Reports

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)
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Supplementary material

SciFeed

Share & Cite This Article

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top