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Mobile robots exploration through cnn-based reinforcement learning
Exploration in an unknown environment is an elemental application for mobile robots. In this paper, we outlined a reinforcement learning method aiming for solving the exploration problem in a corridor environment. The learning model took the depth image from an RGB-D sensor as the only input. The fe...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5177670/ https://www.ncbi.nlm.nih.gov/pubmed/28066702 http://dx.doi.org/10.1186/s40638-016-0055-x |
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author | Tai, Lei Liu, Ming |
author_facet | Tai, Lei Liu, Ming |
author_sort | Tai, Lei |
collection | PubMed |
description | Exploration in an unknown environment is an elemental application for mobile robots. In this paper, we outlined a reinforcement learning method aiming for solving the exploration problem in a corridor environment. The learning model took the depth image from an RGB-D sensor as the only input. The feature representation of the depth image was extracted through a pre-trained convolutional-neural-networks model. Based on the recent success of deep Q-network on artificial intelligence, the robot controller achieved the exploration and obstacle avoidance abilities in several different simulated environments. It is the first time that the reinforcement learning is used to build an exploration strategy for mobile robots through raw sensor information. |
format | Online Article Text |
id | pubmed-5177670 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-51776702017-01-06 Mobile robots exploration through cnn-based reinforcement learning Tai, Lei Liu, Ming Robotics Biomim Research Exploration in an unknown environment is an elemental application for mobile robots. In this paper, we outlined a reinforcement learning method aiming for solving the exploration problem in a corridor environment. The learning model took the depth image from an RGB-D sensor as the only input. The feature representation of the depth image was extracted through a pre-trained convolutional-neural-networks model. Based on the recent success of deep Q-network on artificial intelligence, the robot controller achieved the exploration and obstacle avoidance abilities in several different simulated environments. It is the first time that the reinforcement learning is used to build an exploration strategy for mobile robots through raw sensor information. Springer Berlin Heidelberg 2016-12-21 2016 /pmc/articles/PMC5177670/ /pubmed/28066702 http://dx.doi.org/10.1186/s40638-016-0055-x Text en © The Author(s) 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Research Tai, Lei Liu, Ming Mobile robots exploration through cnn-based reinforcement learning |
title | Mobile robots exploration through cnn-based reinforcement learning |
title_full | Mobile robots exploration through cnn-based reinforcement learning |
title_fullStr | Mobile robots exploration through cnn-based reinforcement learning |
title_full_unstemmed | Mobile robots exploration through cnn-based reinforcement learning |
title_short | Mobile robots exploration through cnn-based reinforcement learning |
title_sort | mobile robots exploration through cnn-based reinforcement learning |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5177670/ https://www.ncbi.nlm.nih.gov/pubmed/28066702 http://dx.doi.org/10.1186/s40638-016-0055-x |
work_keys_str_mv | AT tailei mobilerobotsexplorationthroughcnnbasedreinforcementlearning AT liuming mobilerobotsexplorationthroughcnnbasedreinforcementlearning |