4DoF Rat-SLAM with Memristive Spiking Neural Networks for UAVs Navigation System
- Autores
- Pirozzo, Bernardo; Roark, Geraldina; Ruschetti, Cristian; Villar, Sebastián; De Paula, Mariano; Acosta, Gerardo G.
- Año de publicación
- 2025
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- Unmanned Aerial Vehicles (UAVs) are versatile platforms with potential applications in precision agriculture, disaster management, and more. A core need across these applications is a navigation system that accurately estimates location based on environmental perception. Commercial UAVs use multiple onboard sensors whose fused data improves localization accuracy. The bioinspired Rat-Simultaneous Localization and Mapping (Rat-SLAM) system, is a promising alternative to beexplored to tackle the localization and mapping problem of UAVs. Its cognitive capabilities, semi-metric map construction, and loop closure make it attractive for localization in complex environments. This work presents an improved Rat-SLAM algorithm for UAVs, focusing on three innovations. First, Spiking Neural Networks (SNNs) are incorporated into Rat-SLAM’s core modules to emulate biological processing with greater efficiency. Second, Neuromorphic Computing models the neurons of the SNNs, assessing the feasibility of implementing SNNs on specialized hardware to reduce software processing, a key advantage for UAVs with limited onboard resources. Third, SNNs are developed based on the Memristive Leaky Integrate-and-Fire model, integratingmemristors into artificial neurons to leverage their low power and memory properties. Our approach was evaluated through trajectory simulations using the Hector Quadrotor UAV in the Gazebo environment within the Robot Operating System, yielding valuable insights and guiding future research directions.
- Materia
-
Ingenierías y Tecnologías
Rat-SLAM
Memristors
Neuromorphic Computing
Neuroscience
Spiking Neural Networks
Unmanned Aerial Vehicles - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by/4.0/
- Repositorio
- Institución
- Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
- OAI Identificador
- oai:digital.cic.gba.gob.ar:11746/12514
Ver los metadatos del registro completo
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spelling |
4DoF Rat-SLAM with Memristive Spiking Neural Networks for UAVs Navigation SystemPirozzo, BernardoRoark, GeraldinaRuschetti, CristianVillar, SebastiánDe Paula, MarianoAcosta, Gerardo G.Ingenierías y TecnologíasRat-SLAMMemristorsNeuromorphic ComputingNeuroscienceSpiking Neural NetworksUnmanned Aerial VehiclesUnmanned Aerial Vehicles (UAVs) are versatile platforms with potential applications in precision agriculture, disaster management, and more. A core need across these applications is a navigation system that accurately estimates location based on environmental perception. Commercial UAVs use multiple onboard sensors whose fused data improves localization accuracy. The bioinspired Rat-Simultaneous Localization and Mapping (Rat-SLAM) system, is a promising alternative to beexplored to tackle the localization and mapping problem of UAVs. Its cognitive capabilities, semi-metric map construction, and loop closure make it attractive for localization in complex environments. This work presents an improved Rat-SLAM algorithm for UAVs, focusing on three innovations. First, Spiking Neural Networks (SNNs) are incorporated into Rat-SLAM’s core modules to emulate biological processing with greater efficiency. Second, Neuromorphic Computing models the neurons of the SNNs, assessing the feasibility of implementing SNNs on specialized hardware to reduce software processing, a key advantage for UAVs with limited onboard resources. Third, SNNs are developed based on the Memristive Leaky Integrate-and-Fire model, integratingmemristors into artificial neurons to leverage their low power and memory properties. Our approach was evaluated through trajectory simulations using the Hector Quadrotor UAV in the Gazebo environment within the Robot Operating System, yielding valuable insights and guiding future research directions.2025-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttps://digital.cic.gba.gob.ar/handle/11746/12514enginfo:eu-repo/semantics/altIdentifier/doi/10.70322/dav.2025.10004info:eu-repo/semantics/altIdentifier/issn/2958-7689info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/reponame:CIC Digital (CICBA)instname:Comisión de Investigaciones Científicas de la Provincia de Buenos Airesinstacron:CICBA2025-09-29T13:40:24Zoai:digital.cic.gba.gob.ar:11746/12514Institucionalhttp://digital.cic.gba.gob.arOrganismo científico-tecnológicoNo correspondehttp://digital.cic.gba.gob.ar/oai/snrdmarisa.degiusti@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:94412025-09-29 13:40:25.198CIC Digital (CICBA) - Comisión de Investigaciones Científicas de la Provincia de Buenos Airesfalse |
dc.title.none.fl_str_mv |
4DoF Rat-SLAM with Memristive Spiking Neural Networks for UAVs Navigation System |
title |
4DoF Rat-SLAM with Memristive Spiking Neural Networks for UAVs Navigation System |
spellingShingle |
4DoF Rat-SLAM with Memristive Spiking Neural Networks for UAVs Navigation System Pirozzo, Bernardo Ingenierías y Tecnologías Rat-SLAM Memristors Neuromorphic Computing Neuroscience Spiking Neural Networks Unmanned Aerial Vehicles |
title_short |
4DoF Rat-SLAM with Memristive Spiking Neural Networks for UAVs Navigation System |
title_full |
4DoF Rat-SLAM with Memristive Spiking Neural Networks for UAVs Navigation System |
title_fullStr |
4DoF Rat-SLAM with Memristive Spiking Neural Networks for UAVs Navigation System |
title_full_unstemmed |
4DoF Rat-SLAM with Memristive Spiking Neural Networks for UAVs Navigation System |
title_sort |
4DoF Rat-SLAM with Memristive Spiking Neural Networks for UAVs Navigation System |
dc.creator.none.fl_str_mv |
Pirozzo, Bernardo Roark, Geraldina Ruschetti, Cristian Villar, Sebastián De Paula, Mariano Acosta, Gerardo G. |
author |
Pirozzo, Bernardo |
author_facet |
Pirozzo, Bernardo Roark, Geraldina Ruschetti, Cristian Villar, Sebastián De Paula, Mariano Acosta, Gerardo G. |
author_role |
author |
author2 |
Roark, Geraldina Ruschetti, Cristian Villar, Sebastián De Paula, Mariano Acosta, Gerardo G. |
author2_role |
author author author author author |
dc.subject.none.fl_str_mv |
Ingenierías y Tecnologías Rat-SLAM Memristors Neuromorphic Computing Neuroscience Spiking Neural Networks Unmanned Aerial Vehicles |
topic |
Ingenierías y Tecnologías Rat-SLAM Memristors Neuromorphic Computing Neuroscience Spiking Neural Networks Unmanned Aerial Vehicles |
dc.description.none.fl_txt_mv |
Unmanned Aerial Vehicles (UAVs) are versatile platforms with potential applications in precision agriculture, disaster management, and more. A core need across these applications is a navigation system that accurately estimates location based on environmental perception. Commercial UAVs use multiple onboard sensors whose fused data improves localization accuracy. The bioinspired Rat-Simultaneous Localization and Mapping (Rat-SLAM) system, is a promising alternative to beexplored to tackle the localization and mapping problem of UAVs. Its cognitive capabilities, semi-metric map construction, and loop closure make it attractive for localization in complex environments. This work presents an improved Rat-SLAM algorithm for UAVs, focusing on three innovations. First, Spiking Neural Networks (SNNs) are incorporated into Rat-SLAM’s core modules to emulate biological processing with greater efficiency. Second, Neuromorphic Computing models the neurons of the SNNs, assessing the feasibility of implementing SNNs on specialized hardware to reduce software processing, a key advantage for UAVs with limited onboard resources. Third, SNNs are developed based on the Memristive Leaky Integrate-and-Fire model, integratingmemristors into artificial neurons to leverage their low power and memory properties. Our approach was evaluated through trajectory simulations using the Hector Quadrotor UAV in the Gazebo environment within the Robot Operating System, yielding valuable insights and guiding future research directions. |
description |
Unmanned Aerial Vehicles (UAVs) are versatile platforms with potential applications in precision agriculture, disaster management, and more. A core need across these applications is a navigation system that accurately estimates location based on environmental perception. Commercial UAVs use multiple onboard sensors whose fused data improves localization accuracy. The bioinspired Rat-Simultaneous Localization and Mapping (Rat-SLAM) system, is a promising alternative to beexplored to tackle the localization and mapping problem of UAVs. Its cognitive capabilities, semi-metric map construction, and loop closure make it attractive for localization in complex environments. This work presents an improved Rat-SLAM algorithm for UAVs, focusing on three innovations. First, Spiking Neural Networks (SNNs) are incorporated into Rat-SLAM’s core modules to emulate biological processing with greater efficiency. Second, Neuromorphic Computing models the neurons of the SNNs, assessing the feasibility of implementing SNNs on specialized hardware to reduce software processing, a key advantage for UAVs with limited onboard resources. Third, SNNs are developed based on the Memristive Leaky Integrate-and-Fire model, integratingmemristors into artificial neurons to leverage their low power and memory properties. Our approach was evaluated through trajectory simulations using the Hector Quadrotor UAV in the Gazebo environment within the Robot Operating System, yielding valuable insights and guiding future research directions. |
publishDate |
2025 |
dc.date.none.fl_str_mv |
2025-02 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo |
format |
article |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
https://digital.cic.gba.gob.ar/handle/11746/12514 |
url |
https://digital.cic.gba.gob.ar/handle/11746/12514 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/doi/10.70322/dav.2025.10004 info:eu-repo/semantics/altIdentifier/issn/2958-7689 |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/4.0/ |
eu_rights_str_mv |
openAccess |
rights_invalid_str_mv |
http://creativecommons.org/licenses/by/4.0/ |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:CIC Digital (CICBA) instname:Comisión de Investigaciones Científicas de la Provincia de Buenos Aires instacron:CICBA |
reponame_str |
CIC Digital (CICBA) |
collection |
CIC Digital (CICBA) |
instname_str |
Comisión de Investigaciones Científicas de la Provincia de Buenos Aires |
instacron_str |
CICBA |
institution |
CICBA |
repository.name.fl_str_mv |
CIC Digital (CICBA) - Comisión de Investigaciones Científicas de la Provincia de Buenos Aires |
repository.mail.fl_str_mv |
marisa.degiusti@sedici.unlp.edu.ar |
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13.070432 |