PropEM-L: Radio Propagation Environment Modeling and Learning for Communication-Aware Multi-Robot Exploration Article Swipe
Lillian Clark
,
Jeffrey A. Edlund
,
Marc Sánchez Net
,
Tiago Vaquero
,
Ali‐akbar Agha‐mohammadi
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.15607/rss.2022.xviii.014
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.15607/rss.2022.xviii.014
Multi-robot exploration of complex, unknown environments benefits from the collaboration and cooperation offered by inter-robot communication.Accurate radio signal strength prediction enables communication-aware exploration.Models which ignore the effect of the environment on signal propagation or rely on a priori maps suffer in unknown, communication-restricted (e.g.subterranean) environments.In this work, we present Propagation Environment Modeling and Learning (PropEM-L), a framework which leverages real-time sensor-derived 3D geometric representations of an environment to extract information about line of sight between radios and attenuating walls/obstacles in order to accurately predict received signal strength (RSS).Our data-driven approach combines the strengths of well-known models of signal propagation phenomena (e.g.shadowing, reflection, diffraction) and machine learning, and can adapt online to new environments.We demonstrate the performance of PropEM-L on a six-robot team in a communicationrestricted environment with subway-like, mine-like, and cave-like characteristics, constructed for the 2021 DARPA Subterranean Challenge.Our findings indicate that PropEM-L can improve signal strength prediction accuracy by up to 44% over a logdistance path loss model.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.15607/rss.2022.xviii.014
- https://doi.org/10.15607/rss.2022.xviii.014
- OA Status
- gold
- Cited By
- 10
- References
- 34
- Related Works
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- OpenAlex ID
- https://openalex.org/W4283783984
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4283783984Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.15607/rss.2022.xviii.014Digital Object Identifier
- Title
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PropEM-L: Radio Propagation Environment Modeling and Learning for Communication-Aware Multi-Robot ExplorationWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-06-27Full publication date if available
- Authors
-
Lillian Clark, Jeffrey A. Edlund, Marc Sánchez Net, Tiago Vaquero, Ali‐akbar Agha‐mohammadiList of authors in order
- Landing page
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https://doi.org/10.15607/rss.2022.xviii.014Publisher landing page
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https://doi.org/10.15607/rss.2022.xviii.014Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.15607/rss.2022.xviii.014Direct OA link when available
- Concepts
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Computer science, Robot, Human–computer interaction, Mobile robot, Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
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10Total citation count in OpenAlex
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2025: 4, 2024: 3, 2023: 2, 2022: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.environments.We | 112 |
| abstract_inverted_index.representations | 63 |
| abstract_inverted_index.walls/obstacles | 78 |
| abstract_inverted_index.characteristics, | 131 |
| abstract_inverted_index.(e.g.subterranean) | 43 |
| abstract_inverted_index.exploration.Models | 22 |
| abstract_inverted_index.communication-aware | 21 |
| abstract_inverted_index.communication.Accurate | 15 |
| abstract_inverted_index.communicationrestricted | 124 |
| abstract_inverted_index.communication-restricted | 42 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.9159413 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |