Cargando…
Terrain-aware semantic mapping for cooperative subterranean exploration
Navigation over torturous terrain such as those in natural subterranean environments presents a significant challenge to field robots. The diversity of hazards, from large boulders to muddy or even partially submerged Earth, eludes complete definition. The challenge is amplified if the presence and...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10579614/ https://www.ncbi.nlm.nih.gov/pubmed/37854670 http://dx.doi.org/10.3389/frobt.2023.1249586 |
_version_ | 1785121761318141952 |
---|---|
author | Miles, Michael J. Biggie, Harel Heckman, Christoffer |
author_facet | Miles, Michael J. Biggie, Harel Heckman, Christoffer |
author_sort | Miles, Michael J. |
collection | PubMed |
description | Navigation over torturous terrain such as those in natural subterranean environments presents a significant challenge to field robots. The diversity of hazards, from large boulders to muddy or even partially submerged Earth, eludes complete definition. The challenge is amplified if the presence and nature of these hazards must be shared among multiple agents that are operating in the same space. Furthermore, highly efficient mapping and robust navigation solutions are absolutely critical to operations such as semi-autonomous search and rescue. We propose an efficient and modular framework for semantic grid mapping of subterranean environments. Our approach encodes occupancy and traversability information, as well as the presence of stairways, into a grid map that is distributed amongst a robot fleet despite bandwidth constraints. We demonstrate that the mapping method enables safe and enduring exploration of subterranean environments. The performance of the system is showcased in high-fidelity simulations, physical experiments, and Team MARBLE’s entry in the DARPA Subterranean Challenge which received third place. |
format | Online Article Text |
id | pubmed-10579614 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-105796142023-10-18 Terrain-aware semantic mapping for cooperative subterranean exploration Miles, Michael J. Biggie, Harel Heckman, Christoffer Front Robot AI Robotics and AI Navigation over torturous terrain such as those in natural subterranean environments presents a significant challenge to field robots. The diversity of hazards, from large boulders to muddy or even partially submerged Earth, eludes complete definition. The challenge is amplified if the presence and nature of these hazards must be shared among multiple agents that are operating in the same space. Furthermore, highly efficient mapping and robust navigation solutions are absolutely critical to operations such as semi-autonomous search and rescue. We propose an efficient and modular framework for semantic grid mapping of subterranean environments. Our approach encodes occupancy and traversability information, as well as the presence of stairways, into a grid map that is distributed amongst a robot fleet despite bandwidth constraints. We demonstrate that the mapping method enables safe and enduring exploration of subterranean environments. The performance of the system is showcased in high-fidelity simulations, physical experiments, and Team MARBLE’s entry in the DARPA Subterranean Challenge which received third place. Frontiers Media S.A. 2023-10-03 /pmc/articles/PMC10579614/ /pubmed/37854670 http://dx.doi.org/10.3389/frobt.2023.1249586 Text en Copyright © 2023 Miles, Biggie and Heckman. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Robotics and AI Miles, Michael J. Biggie, Harel Heckman, Christoffer Terrain-aware semantic mapping for cooperative subterranean exploration |
title | Terrain-aware semantic mapping for cooperative subterranean exploration |
title_full | Terrain-aware semantic mapping for cooperative subterranean exploration |
title_fullStr | Terrain-aware semantic mapping for cooperative subterranean exploration |
title_full_unstemmed | Terrain-aware semantic mapping for cooperative subterranean exploration |
title_short | Terrain-aware semantic mapping for cooperative subterranean exploration |
title_sort | terrain-aware semantic mapping for cooperative subterranean exploration |
topic | Robotics and AI |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10579614/ https://www.ncbi.nlm.nih.gov/pubmed/37854670 http://dx.doi.org/10.3389/frobt.2023.1249586 |
work_keys_str_mv | AT milesmichaelj terrainawaresemanticmappingforcooperativesubterraneanexploration AT biggieharel terrainawaresemanticmappingforcooperativesubterraneanexploration AT heckmanchristoffer terrainawaresemanticmappingforcooperativesubterraneanexploration |