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Real-Time Avoidance of Ionising Radiation Using Layered Costmaps for Mobile Robots
Humans in hazardous environments take actions to reduce unnecessary risk, including limiting exposure to radioactive materials where ionising radiation can be a threat to human health. Robots can adopt the same approach of risk avoidance to minimise exposure to radiation, therefore limiting damage t...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8968874/ https://www.ncbi.nlm.nih.gov/pubmed/35368431 http://dx.doi.org/10.3389/frobt.2022.862067 |
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author | West, Andrew Wright, Thomas Tsitsimpelis, Ioannis Groves, Keir Joyce, Malcolm J. Lennox, Barry |
author_facet | West, Andrew Wright, Thomas Tsitsimpelis, Ioannis Groves, Keir Joyce, Malcolm J. Lennox, Barry |
author_sort | West, Andrew |
collection | PubMed |
description | Humans in hazardous environments take actions to reduce unnecessary risk, including limiting exposure to radioactive materials where ionising radiation can be a threat to human health. Robots can adopt the same approach of risk avoidance to minimise exposure to radiation, therefore limiting damage to electronics and materials. Reducing a robot’s exposure to radiation results in longer operational lifetime and better return on investment for nuclear sector stakeholders. This work achieves radiation avoidance through the use of layered costmaps, to inform path planning algorithms of this additional risk. Interpolation of radiation observations into the configuration space of the robot is accomplished using an inverse distance weighting approach. This technique was successfully demonstrated using an unmanned ground vehicle running the Robot Operating System equipped with compatible gamma radiation sensors, both in simulation and in real-world mock inspection missions, where the vehicle was exposed to radioactive materials in Lancaster University’s Neutron Laboratory. The addition of radiation avoidance functionality was shown to reduce total accumulated dose to background levels in real-world deployment and up to a factor of 10 in simulation. |
format | Online Article Text |
id | pubmed-8968874 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-89688742022-04-01 Real-Time Avoidance of Ionising Radiation Using Layered Costmaps for Mobile Robots West, Andrew Wright, Thomas Tsitsimpelis, Ioannis Groves, Keir Joyce, Malcolm J. Lennox, Barry Front Robot AI Robotics and AI Humans in hazardous environments take actions to reduce unnecessary risk, including limiting exposure to radioactive materials where ionising radiation can be a threat to human health. Robots can adopt the same approach of risk avoidance to minimise exposure to radiation, therefore limiting damage to electronics and materials. Reducing a robot’s exposure to radiation results in longer operational lifetime and better return on investment for nuclear sector stakeholders. This work achieves radiation avoidance through the use of layered costmaps, to inform path planning algorithms of this additional risk. Interpolation of radiation observations into the configuration space of the robot is accomplished using an inverse distance weighting approach. This technique was successfully demonstrated using an unmanned ground vehicle running the Robot Operating System equipped with compatible gamma radiation sensors, both in simulation and in real-world mock inspection missions, where the vehicle was exposed to radioactive materials in Lancaster University’s Neutron Laboratory. The addition of radiation avoidance functionality was shown to reduce total accumulated dose to background levels in real-world deployment and up to a factor of 10 in simulation. Frontiers Media S.A. 2022-03-17 /pmc/articles/PMC8968874/ /pubmed/35368431 http://dx.doi.org/10.3389/frobt.2022.862067 Text en Copyright © 2022 West, Wright, Tsitsimpelis, Groves, Joyce and Lennox. 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 West, Andrew Wright, Thomas Tsitsimpelis, Ioannis Groves, Keir Joyce, Malcolm J. Lennox, Barry Real-Time Avoidance of Ionising Radiation Using Layered Costmaps for Mobile Robots |
title | Real-Time Avoidance of Ionising Radiation Using Layered Costmaps for Mobile Robots |
title_full | Real-Time Avoidance of Ionising Radiation Using Layered Costmaps for Mobile Robots |
title_fullStr | Real-Time Avoidance of Ionising Radiation Using Layered Costmaps for Mobile Robots |
title_full_unstemmed | Real-Time Avoidance of Ionising Radiation Using Layered Costmaps for Mobile Robots |
title_short | Real-Time Avoidance of Ionising Radiation Using Layered Costmaps for Mobile Robots |
title_sort | real-time avoidance of ionising radiation using layered costmaps for mobile robots |
topic | Robotics and AI |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8968874/ https://www.ncbi.nlm.nih.gov/pubmed/35368431 http://dx.doi.org/10.3389/frobt.2022.862067 |
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