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Lidar-Based Navigation of Subterranean Environments Using Bio-Inspired Wide-Field Integration of Nearness
Navigating unknown environments is an ongoing challenge in robotics. Processing large amounts of sensor data to maintain localization, maps of the environment, and sensible paths can result in high compute loads and lower maximum vehicle speeds. This paper presents a bio-inspired algorithm for effic...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8840438/ https://www.ncbi.nlm.nih.gov/pubmed/35161595 http://dx.doi.org/10.3390/s22030849 |
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author | Ohradzansky, Michael T. Humbert, J. Sean |
author_facet | Ohradzansky, Michael T. Humbert, J. Sean |
author_sort | Ohradzansky, Michael T. |
collection | PubMed |
description | Navigating unknown environments is an ongoing challenge in robotics. Processing large amounts of sensor data to maintain localization, maps of the environment, and sensible paths can result in high compute loads and lower maximum vehicle speeds. This paper presents a bio-inspired algorithm for efficiently processing depth measurements to achieve fast navigation of unknown subterranean environments. Animals developed efficient sensorimotor convergence approaches, allowing for rapid processing of large numbers of spatially distributed measurements into signals relevant for different behavioral responses necessary to their survival. Using a spatial inner-product to model this sensorimotor convergence principle, environmentally relative states critical to navigation are extracted from spatially distributed depth measurements using derived weighting functions. These states are then applied as feedback to control a simulated quadrotor platform, enabling autonomous navigation in subterranean environments. The resulting outer-loop velocity controller is demonstrated in both a generalized subterranean environment, represented by an infinite cylinder, and nongeneralized environments like tunnels and caves. |
format | Online Article Text |
id | pubmed-8840438 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-88404382022-02-13 Lidar-Based Navigation of Subterranean Environments Using Bio-Inspired Wide-Field Integration of Nearness Ohradzansky, Michael T. Humbert, J. Sean Sensors (Basel) Article Navigating unknown environments is an ongoing challenge in robotics. Processing large amounts of sensor data to maintain localization, maps of the environment, and sensible paths can result in high compute loads and lower maximum vehicle speeds. This paper presents a bio-inspired algorithm for efficiently processing depth measurements to achieve fast navigation of unknown subterranean environments. Animals developed efficient sensorimotor convergence approaches, allowing for rapid processing of large numbers of spatially distributed measurements into signals relevant for different behavioral responses necessary to their survival. Using a spatial inner-product to model this sensorimotor convergence principle, environmentally relative states critical to navigation are extracted from spatially distributed depth measurements using derived weighting functions. These states are then applied as feedback to control a simulated quadrotor platform, enabling autonomous navigation in subterranean environments. The resulting outer-loop velocity controller is demonstrated in both a generalized subterranean environment, represented by an infinite cylinder, and nongeneralized environments like tunnels and caves. MDPI 2022-01-23 /pmc/articles/PMC8840438/ /pubmed/35161595 http://dx.doi.org/10.3390/s22030849 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Ohradzansky, Michael T. Humbert, J. Sean Lidar-Based Navigation of Subterranean Environments Using Bio-Inspired Wide-Field Integration of Nearness |
title | Lidar-Based Navigation of Subterranean Environments Using Bio-Inspired Wide-Field Integration of Nearness |
title_full | Lidar-Based Navigation of Subterranean Environments Using Bio-Inspired Wide-Field Integration of Nearness |
title_fullStr | Lidar-Based Navigation of Subterranean Environments Using Bio-Inspired Wide-Field Integration of Nearness |
title_full_unstemmed | Lidar-Based Navigation of Subterranean Environments Using Bio-Inspired Wide-Field Integration of Nearness |
title_short | Lidar-Based Navigation of Subterranean Environments Using Bio-Inspired Wide-Field Integration of Nearness |
title_sort | lidar-based navigation of subterranean environments using bio-inspired wide-field integration of nearness |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8840438/ https://www.ncbi.nlm.nih.gov/pubmed/35161595 http://dx.doi.org/10.3390/s22030849 |
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