Neuromorphic Perception and Navigation for Mobile Robots: A Review

With the fast and unstoppable evolution of robotics and artificial intelligence, effective autonomous navigation in real-world scenarios has become one of the most pressing challenges in the literature. However, demanding requirements, such as real-time operation, energy and computational efficiency...

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Detalles Bibliográficos
Autores: Novo, Alvaro, Lobon, Francisco, Garcia de Marina, Hector, Romero, Samuel, Barranco, Francisco
Tipo de recurso: artículo
Fecha de publicación:2024
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/372471
Acceso en línea:http://hdl.handle.net/10261/372471
Access Level:acceso abierto
Palabra clave:Navigation
Hippocampus
Neuromorphic sensors
Brain inspired
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spelling Neuromorphic Perception and Navigation for Mobile Robots: A ReviewNovo, AlvaroLobon, FranciscoGarcia de Marina, HectorRomero, SamuelBarranco, FranciscoNavigationHippocampusNeuromorphic sensorsBrain inspiredWith the fast and unstoppable evolution of robotics and artificial intelligence, effective autonomous navigation in real-world scenarios has become one of the most pressing challenges in the literature. However, demanding requirements, such as real-time operation, energy and computational efficiency, robustness, and reliability, make most current solutions unsuitable for real-world challenges. Thus, researchers are fostered to seek innovative approaches, such as bio-inspired solutions. Indeed, animals have the intrinsic ability to efficiently perceive, understand, and navigate their unstructured surroundings. To do so, they exploit self-motion cues, proprioception, and visual flow in a cognitive process to map their environment and locate themselves within it. Computational neuroscientists aim to answer "how"and "why"such cognitive processes occur in the brain, to design novel neuromorphic sensors and methods that imitate biological processing. This survey aims to comprehensively review the application of brain-inspired strategies to autonomous navigation. The paper delves into areas such as neuromorphic perception, asynchronous event processing, energy-efficient and adaptive learning, and the emulation of brain regions vital for navigation, such as the hippocampus and entorhinal cortex. © 2024 ACM, Inc.This work was supported by the Spanish National Grant PID2022-141466OB-I00 funded by MICIU/AEI/10.13039/501100011033 and by ERDF/EU.Peer reviewedAssociation for Computing MachineryMinisterio de Ciencia, Innovación y Universidades (España)European CommissionAgencia Estatal de Investigación (España)Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]2024202420242024info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_dcae04bchttp://hdl.handle.net/10261/372471reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2022-141466OB-I00http://dx.doi.org/10.1145/3656469Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3724712026-05-22T06:33:51Z
dc.title.none.fl_str_mv Neuromorphic Perception and Navigation for Mobile Robots: A Review
title Neuromorphic Perception and Navigation for Mobile Robots: A Review
spellingShingle Neuromorphic Perception and Navigation for Mobile Robots: A Review
Novo, Alvaro
Navigation
Hippocampus
Neuromorphic sensors
Brain inspired
title_short Neuromorphic Perception and Navigation for Mobile Robots: A Review
title_full Neuromorphic Perception and Navigation for Mobile Robots: A Review
title_fullStr Neuromorphic Perception and Navigation for Mobile Robots: A Review
title_full_unstemmed Neuromorphic Perception and Navigation for Mobile Robots: A Review
title_sort Neuromorphic Perception and Navigation for Mobile Robots: A Review
dc.creator.none.fl_str_mv Novo, Alvaro
Lobon, Francisco
Garcia de Marina, Hector
Romero, Samuel
Barranco, Francisco
author Novo, Alvaro
author_facet Novo, Alvaro
Lobon, Francisco
Garcia de Marina, Hector
Romero, Samuel
Barranco, Francisco
author_role author
author2 Lobon, Francisco
Garcia de Marina, Hector
Romero, Samuel
Barranco, Francisco
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Ministerio de Ciencia, Innovación y Universidades (España)
European Commission
Agencia Estatal de Investigación (España)
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Navigation
Hippocampus
Neuromorphic sensors
Brain inspired
topic Navigation
Hippocampus
Neuromorphic sensors
Brain inspired
description With the fast and unstoppable evolution of robotics and artificial intelligence, effective autonomous navigation in real-world scenarios has become one of the most pressing challenges in the literature. However, demanding requirements, such as real-time operation, energy and computational efficiency, robustness, and reliability, make most current solutions unsuitable for real-world challenges. Thus, researchers are fostered to seek innovative approaches, such as bio-inspired solutions. Indeed, animals have the intrinsic ability to efficiently perceive, understand, and navigate their unstructured surroundings. To do so, they exploit self-motion cues, proprioception, and visual flow in a cognitive process to map their environment and locate themselves within it. Computational neuroscientists aim to answer "how"and "why"such cognitive processes occur in the brain, to design novel neuromorphic sensors and methods that imitate biological processing. This survey aims to comprehensively review the application of brain-inspired strategies to autonomous navigation. The paper delves into areas such as neuromorphic perception, asynchronous event processing, energy-efficient and adaptive learning, and the emulation of brain regions vital for navigation, such as the hippocampus and entorhinal cortex. © 2024 ACM, Inc.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
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format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/372471
url http://hdl.handle.net/10261/372471
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2022-141466OB-I00
http://dx.doi.org/10.1145/3656469
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Association for Computing Machinery
publisher.none.fl_str_mv Association for Computing Machinery
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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