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Distributed Multi-Robot Information Gathering under Spatio-Temporal Inter-Robot Constraints†
Information gathering (IG) algorithms aim to intelligently select the mobile robotic sensor actions required to efficiently obtain an accurate reconstruction of a physical process, such as an occupancy map, a wind field, or a magnetic field. Recently, multiple IG algorithms that benefit from multi-r...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7013982/ https://www.ncbi.nlm.nih.gov/pubmed/31952178 http://dx.doi.org/10.3390/s20020484 |
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author | Viseras, Alberto Xu, Zhe Merino, Luis |
author_facet | Viseras, Alberto Xu, Zhe Merino, Luis |
author_sort | Viseras, Alberto |
collection | PubMed |
description | Information gathering (IG) algorithms aim to intelligently select the mobile robotic sensor actions required to efficiently obtain an accurate reconstruction of a physical process, such as an occupancy map, a wind field, or a magnetic field. Recently, multiple IG algorithms that benefit from multi-robot cooperation have been proposed in the literature. Most of these algorithms employ discretization of the state and action spaces, which makes them computationally intractable for robotic systems with complex dynamics. Moreover, they cannot deal with inter-robot restrictions such as collision avoidance or communication constraints. This paper presents a novel approach for multi-robot information gathering (MR-IG) that tackles the two aforementioned restrictions: (i) discretization of robot’s state space, and (ii) dealing with inter-robot constraints. Here we propose an algorithm that employs: (i) an underlying model of the physical process of interest, (ii) sampling-based planners to plan paths in a continuous domain, and (iii) a distributed decision-making algorithm to enable multi-robot coordination. In particular, we use the max-sum algorithm for distributed decision-making by defining an information-theoretic utility function. This function maximizes IG, while fulfilling inter-robot communication and collision avoidance constraints. We validate our proposed approach in simulations, and in a field experiment where three quadcopters explore a simulated wind field. Results demonstrate the effectiveness and scalability with respect to the number of robots of our approach. |
format | Online Article Text |
id | pubmed-7013982 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-70139822020-03-09 Distributed Multi-Robot Information Gathering under Spatio-Temporal Inter-Robot Constraints† Viseras, Alberto Xu, Zhe Merino, Luis Sensors (Basel) Article Information gathering (IG) algorithms aim to intelligently select the mobile robotic sensor actions required to efficiently obtain an accurate reconstruction of a physical process, such as an occupancy map, a wind field, or a magnetic field. Recently, multiple IG algorithms that benefit from multi-robot cooperation have been proposed in the literature. Most of these algorithms employ discretization of the state and action spaces, which makes them computationally intractable for robotic systems with complex dynamics. Moreover, they cannot deal with inter-robot restrictions such as collision avoidance or communication constraints. This paper presents a novel approach for multi-robot information gathering (MR-IG) that tackles the two aforementioned restrictions: (i) discretization of robot’s state space, and (ii) dealing with inter-robot constraints. Here we propose an algorithm that employs: (i) an underlying model of the physical process of interest, (ii) sampling-based planners to plan paths in a continuous domain, and (iii) a distributed decision-making algorithm to enable multi-robot coordination. In particular, we use the max-sum algorithm for distributed decision-making by defining an information-theoretic utility function. This function maximizes IG, while fulfilling inter-robot communication and collision avoidance constraints. We validate our proposed approach in simulations, and in a field experiment where three quadcopters explore a simulated wind field. Results demonstrate the effectiveness and scalability with respect to the number of robots of our approach. MDPI 2020-01-15 /pmc/articles/PMC7013982/ /pubmed/31952178 http://dx.doi.org/10.3390/s20020484 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Viseras, Alberto Xu, Zhe Merino, Luis Distributed Multi-Robot Information Gathering under Spatio-Temporal Inter-Robot Constraints† |
title | Distributed Multi-Robot Information Gathering under Spatio-Temporal Inter-Robot Constraints† |
title_full | Distributed Multi-Robot Information Gathering under Spatio-Temporal Inter-Robot Constraints† |
title_fullStr | Distributed Multi-Robot Information Gathering under Spatio-Temporal Inter-Robot Constraints† |
title_full_unstemmed | Distributed Multi-Robot Information Gathering under Spatio-Temporal Inter-Robot Constraints† |
title_short | Distributed Multi-Robot Information Gathering under Spatio-Temporal Inter-Robot Constraints† |
title_sort | distributed multi-robot information gathering under spatio-temporal inter-robot constraints† |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7013982/ https://www.ncbi.nlm.nih.gov/pubmed/31952178 http://dx.doi.org/10.3390/s20020484 |
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