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Bionic Path Planning Fusing Episodic Memory Based on RatSLAM

Inspired by rodents’ ability to navigate freely in a given space, bionavigation systems provide alternatives to traditional probabilistic solutions. This paper proposed a bionic path planning method based on RatSLAM to provide a novel viewpoint for robots to make a more flexible and intelligent navi...

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Detalles Bibliográficos
Autores principales: Yu, Shumei, Xu, Haidong, Wu, Chong, Jiang, Xin, Sun, Rongchuan, Sun, Lining
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9944839/
https://www.ncbi.nlm.nih.gov/pubmed/36810390
http://dx.doi.org/10.3390/biomimetics8010059
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author Yu, Shumei
Xu, Haidong
Wu, Chong
Jiang, Xin
Sun, Rongchuan
Sun, Lining
author_facet Yu, Shumei
Xu, Haidong
Wu, Chong
Jiang, Xin
Sun, Rongchuan
Sun, Lining
author_sort Yu, Shumei
collection PubMed
description Inspired by rodents’ ability to navigate freely in a given space, bionavigation systems provide alternatives to traditional probabilistic solutions. This paper proposed a bionic path planning method based on RatSLAM to provide a novel viewpoint for robots to make a more flexible and intelligent navigation scheme. A neural network fusing historic episodic memory was proposed to improve the connectivity of the episodic cognitive map. It is biomimetically important to generate an episodic cognitive map and establish a one-to-one correspondence between the events generated by episodic memory and the visual template of RatSLAM. The episodic cognitive map can be improved by imitating the rodents’ behavior of memory fusion to produce better path planning results. The experimental results of different scenarios illustrate that the proposed method identified the connectivity between way points, optimized the result of path planning, and improved the flexibility of the system.
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spelling pubmed-99448392023-02-23 Bionic Path Planning Fusing Episodic Memory Based on RatSLAM Yu, Shumei Xu, Haidong Wu, Chong Jiang, Xin Sun, Rongchuan Sun, Lining Biomimetics (Basel) Article Inspired by rodents’ ability to navigate freely in a given space, bionavigation systems provide alternatives to traditional probabilistic solutions. This paper proposed a bionic path planning method based on RatSLAM to provide a novel viewpoint for robots to make a more flexible and intelligent navigation scheme. A neural network fusing historic episodic memory was proposed to improve the connectivity of the episodic cognitive map. It is biomimetically important to generate an episodic cognitive map and establish a one-to-one correspondence between the events generated by episodic memory and the visual template of RatSLAM. The episodic cognitive map can be improved by imitating the rodents’ behavior of memory fusion to produce better path planning results. The experimental results of different scenarios illustrate that the proposed method identified the connectivity between way points, optimized the result of path planning, and improved the flexibility of the system. MDPI 2023-02-01 /pmc/articles/PMC9944839/ /pubmed/36810390 http://dx.doi.org/10.3390/biomimetics8010059 Text en © 2023 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
Yu, Shumei
Xu, Haidong
Wu, Chong
Jiang, Xin
Sun, Rongchuan
Sun, Lining
Bionic Path Planning Fusing Episodic Memory Based on RatSLAM
title Bionic Path Planning Fusing Episodic Memory Based on RatSLAM
title_full Bionic Path Planning Fusing Episodic Memory Based on RatSLAM
title_fullStr Bionic Path Planning Fusing Episodic Memory Based on RatSLAM
title_full_unstemmed Bionic Path Planning Fusing Episodic Memory Based on RatSLAM
title_short Bionic Path Planning Fusing Episodic Memory Based on RatSLAM
title_sort bionic path planning fusing episodic memory based on ratslam
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9944839/
https://www.ncbi.nlm.nih.gov/pubmed/36810390
http://dx.doi.org/10.3390/biomimetics8010059
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