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Scalable Gas Sensing, Mapping, and Path Planning via Decentralized Hilbert Maps
This paper develops a decentralized approach to gas distribution mapping (GDM) and information-driven path planning for large-scale distributed sensing systems. Gas mapping is performed using a probabilistic representation known as a Hilbert map, which formulates the mapping problem as a multi-class...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6479848/ https://www.ncbi.nlm.nih.gov/pubmed/30925833 http://dx.doi.org/10.3390/s19071524 |
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author | Zhu, Pingping Ferrari, Silvia Morelli, Julian Linares, Richard Doerr, Bryce |
author_facet | Zhu, Pingping Ferrari, Silvia Morelli, Julian Linares, Richard Doerr, Bryce |
author_sort | Zhu, Pingping |
collection | PubMed |
description | This paper develops a decentralized approach to gas distribution mapping (GDM) and information-driven path planning for large-scale distributed sensing systems. Gas mapping is performed using a probabilistic representation known as a Hilbert map, which formulates the mapping problem as a multi-class classification task and uses kernel logistic regression to train a discriminative classifier online. A novel Hilbert map information fusion method is presented for rapidly merging the information from individual robot maps using limited data communication. A communication strategy that implements data fusion among many robots is also presented for the decentralized computation of GDMs. New entropy-based information-driven path-planning methods are developed and compared to existing approaches, such as particle swarm optimization (PSO) and random walks (RW). Numerical experiments conducted in simulated indoor and outdoor environments show that the information-driven approaches proposed in this paper far outperform other approaches, and avoid mutual collisions in real time. |
format | Online Article Text |
id | pubmed-6479848 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-64798482019-04-29 Scalable Gas Sensing, Mapping, and Path Planning via Decentralized Hilbert Maps Zhu, Pingping Ferrari, Silvia Morelli, Julian Linares, Richard Doerr, Bryce Sensors (Basel) Article This paper develops a decentralized approach to gas distribution mapping (GDM) and information-driven path planning for large-scale distributed sensing systems. Gas mapping is performed using a probabilistic representation known as a Hilbert map, which formulates the mapping problem as a multi-class classification task and uses kernel logistic regression to train a discriminative classifier online. A novel Hilbert map information fusion method is presented for rapidly merging the information from individual robot maps using limited data communication. A communication strategy that implements data fusion among many robots is also presented for the decentralized computation of GDMs. New entropy-based information-driven path-planning methods are developed and compared to existing approaches, such as particle swarm optimization (PSO) and random walks (RW). Numerical experiments conducted in simulated indoor and outdoor environments show that the information-driven approaches proposed in this paper far outperform other approaches, and avoid mutual collisions in real time. MDPI 2019-03-28 /pmc/articles/PMC6479848/ /pubmed/30925833 http://dx.doi.org/10.3390/s19071524 Text en © 2019 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 Zhu, Pingping Ferrari, Silvia Morelli, Julian Linares, Richard Doerr, Bryce Scalable Gas Sensing, Mapping, and Path Planning via Decentralized Hilbert Maps |
title | Scalable Gas Sensing, Mapping, and Path Planning via Decentralized Hilbert Maps |
title_full | Scalable Gas Sensing, Mapping, and Path Planning via Decentralized Hilbert Maps |
title_fullStr | Scalable Gas Sensing, Mapping, and Path Planning via Decentralized Hilbert Maps |
title_full_unstemmed | Scalable Gas Sensing, Mapping, and Path Planning via Decentralized Hilbert Maps |
title_short | Scalable Gas Sensing, Mapping, and Path Planning via Decentralized Hilbert Maps |
title_sort | scalable gas sensing, mapping, and path planning via decentralized hilbert maps |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6479848/ https://www.ncbi.nlm.nih.gov/pubmed/30925833 http://dx.doi.org/10.3390/s19071524 |
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