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An Integrated Strategy for Autonomous Exploration of Spatial Processes in Unknown Environments
Exploration of spatial processes, such as radioactivity or temperature is a fundamental task in many robotic applications. In the literature, robotic exploration is mainly carried out for applications where the environment is a priori known. However, for most real life applications this assumption o...
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/PMC7374370/ https://www.ncbi.nlm.nih.gov/pubmed/32629898 http://dx.doi.org/10.3390/s20133663 |
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author | Karolj, Valentina Viseras, Alberto Merino, Luis Shutin, Dmitriy |
author_facet | Karolj, Valentina Viseras, Alberto Merino, Luis Shutin, Dmitriy |
author_sort | Karolj, Valentina |
collection | PubMed |
description | Exploration of spatial processes, such as radioactivity or temperature is a fundamental task in many robotic applications. In the literature, robotic exploration is mainly carried out for applications where the environment is a priori known. However, for most real life applications this assumption often does not hold, specifically for disaster scenarios. In this paper, we propose a novel integrated strategy that allows a robot to explore a spatial process of interest in an unknown environment. To this end, we build upon two major blocks. First, we propose the use of GP to model the spatial process of interest, and process entropy to drive the exploration. Second, we employ registration algorithms for robot mapping and localization, and frontier-based exploration to explore the environment. However, map and process exploration can be conflicting goals. Our integrated strategy fuses the two aforementioned blocks through a trade-off between process and map exploration. We carry out extensive evaluations of our algorithm in simulated environments with respect to different baselines and environment setups using simulated GP data as a process at hand. Additionally, we perform experimental verification with a mobile holonomic robot exploring a simulated process in an unknown labyrinth environment. Demonstrated results show that our integrated strategy outperforms both frontier-based and GP entropy-driven exploration strategies. |
format | Online Article Text |
id | pubmed-7374370 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-73743702020-08-06 An Integrated Strategy for Autonomous Exploration of Spatial Processes in Unknown Environments Karolj, Valentina Viseras, Alberto Merino, Luis Shutin, Dmitriy Sensors (Basel) Article Exploration of spatial processes, such as radioactivity or temperature is a fundamental task in many robotic applications. In the literature, robotic exploration is mainly carried out for applications where the environment is a priori known. However, for most real life applications this assumption often does not hold, specifically for disaster scenarios. In this paper, we propose a novel integrated strategy that allows a robot to explore a spatial process of interest in an unknown environment. To this end, we build upon two major blocks. First, we propose the use of GP to model the spatial process of interest, and process entropy to drive the exploration. Second, we employ registration algorithms for robot mapping and localization, and frontier-based exploration to explore the environment. However, map and process exploration can be conflicting goals. Our integrated strategy fuses the two aforementioned blocks through a trade-off between process and map exploration. We carry out extensive evaluations of our algorithm in simulated environments with respect to different baselines and environment setups using simulated GP data as a process at hand. Additionally, we perform experimental verification with a mobile holonomic robot exploring a simulated process in an unknown labyrinth environment. Demonstrated results show that our integrated strategy outperforms both frontier-based and GP entropy-driven exploration strategies. MDPI 2020-06-30 /pmc/articles/PMC7374370/ /pubmed/32629898 http://dx.doi.org/10.3390/s20133663 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 Karolj, Valentina Viseras, Alberto Merino, Luis Shutin, Dmitriy An Integrated Strategy for Autonomous Exploration of Spatial Processes in Unknown Environments |
title | An Integrated Strategy for Autonomous Exploration of Spatial Processes in Unknown Environments |
title_full | An Integrated Strategy for Autonomous Exploration of Spatial Processes in Unknown Environments |
title_fullStr | An Integrated Strategy for Autonomous Exploration of Spatial Processes in Unknown Environments |
title_full_unstemmed | An Integrated Strategy for Autonomous Exploration of Spatial Processes in Unknown Environments |
title_short | An Integrated Strategy for Autonomous Exploration of Spatial Processes in Unknown Environments |
title_sort | integrated strategy for autonomous exploration of spatial processes in unknown environments |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7374370/ https://www.ncbi.nlm.nih.gov/pubmed/32629898 http://dx.doi.org/10.3390/s20133663 |
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