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Ring attractor bio-inspired neural network for social robot navigation

INTRODUCTION: We introduce a bio-inspired navigation system for a robot to guide a social agent to a target location while avoiding static and dynamic obstacles. Robot navigation can be accomplished through a model of ring attractor neural networks. This connectivity pattern between neurons enables...

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Autores principales: Rivero-Ortega, Jesús D., Mosquera-Maturana, Juan S., Pardo-Cabrera, Josh, Hurtado-López, Julián, Hernández, Juan D., Romero-Cano, Victor, Ramírez-Moreno, David F.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10501606/
https://www.ncbi.nlm.nih.gov/pubmed/37719331
http://dx.doi.org/10.3389/fnbot.2023.1211570
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author Rivero-Ortega, Jesús D.
Mosquera-Maturana, Juan S.
Pardo-Cabrera, Josh
Hurtado-López, Julián
Hernández, Juan D.
Romero-Cano, Victor
Ramírez-Moreno, David F.
author_facet Rivero-Ortega, Jesús D.
Mosquera-Maturana, Juan S.
Pardo-Cabrera, Josh
Hurtado-López, Julián
Hernández, Juan D.
Romero-Cano, Victor
Ramírez-Moreno, David F.
author_sort Rivero-Ortega, Jesús D.
collection PubMed
description INTRODUCTION: We introduce a bio-inspired navigation system for a robot to guide a social agent to a target location while avoiding static and dynamic obstacles. Robot navigation can be accomplished through a model of ring attractor neural networks. This connectivity pattern between neurons enables the generation of stable activity patterns that can represent continuous variables such as heading direction or position. The integration of sensory representation, decision-making, and motor control through ring attractor networks offers a biologically-inspired approach to navigation in complex environments. METHODS: The navigation system is divided into perception, planning, and control stages. Our approach is compared to the widely-used Social Force Model and Rapidly Exploring Random Tree Star methods using the Social Individual Index and Relative Motion Index as metrics in simulated experiments. We created a virtual scenario of a pedestrian area with various obstacles and dynamic agents. RESULTS: The results obtained in our experiments demonstrate the effectiveness of this architecture in guiding a social agent while avoiding obstacles, and the metrics used for evaluating the system indicate that our proposal outperforms the widely used Social Force Model. DISCUSSION: Our approach points to improving safety and comfort specifically for human-robot interactions. By integrating the Social Individual Index and Relative Motion Index, this approach considers both social comfort and collision avoidance features, resulting in better human-robot interactions in a crowded environment.
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spelling pubmed-105016062023-09-15 Ring attractor bio-inspired neural network for social robot navigation Rivero-Ortega, Jesús D. Mosquera-Maturana, Juan S. Pardo-Cabrera, Josh Hurtado-López, Julián Hernández, Juan D. Romero-Cano, Victor Ramírez-Moreno, David F. Front Neurorobot Neuroscience INTRODUCTION: We introduce a bio-inspired navigation system for a robot to guide a social agent to a target location while avoiding static and dynamic obstacles. Robot navigation can be accomplished through a model of ring attractor neural networks. This connectivity pattern between neurons enables the generation of stable activity patterns that can represent continuous variables such as heading direction or position. The integration of sensory representation, decision-making, and motor control through ring attractor networks offers a biologically-inspired approach to navigation in complex environments. METHODS: The navigation system is divided into perception, planning, and control stages. Our approach is compared to the widely-used Social Force Model and Rapidly Exploring Random Tree Star methods using the Social Individual Index and Relative Motion Index as metrics in simulated experiments. We created a virtual scenario of a pedestrian area with various obstacles and dynamic agents. RESULTS: The results obtained in our experiments demonstrate the effectiveness of this architecture in guiding a social agent while avoiding obstacles, and the metrics used for evaluating the system indicate that our proposal outperforms the widely used Social Force Model. DISCUSSION: Our approach points to improving safety and comfort specifically for human-robot interactions. By integrating the Social Individual Index and Relative Motion Index, this approach considers both social comfort and collision avoidance features, resulting in better human-robot interactions in a crowded environment. Frontiers Media S.A. 2023-08-31 /pmc/articles/PMC10501606/ /pubmed/37719331 http://dx.doi.org/10.3389/fnbot.2023.1211570 Text en Copyright © 2023 Rivero-Ortega, Mosquera-Maturana, Pardo-Cabrera, Hurtado-López, Hernández, Romero-Cano and Ramírez-Moreno. 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 Neuroscience
Rivero-Ortega, Jesús D.
Mosquera-Maturana, Juan S.
Pardo-Cabrera, Josh
Hurtado-López, Julián
Hernández, Juan D.
Romero-Cano, Victor
Ramírez-Moreno, David F.
Ring attractor bio-inspired neural network for social robot navigation
title Ring attractor bio-inspired neural network for social robot navigation
title_full Ring attractor bio-inspired neural network for social robot navigation
title_fullStr Ring attractor bio-inspired neural network for social robot navigation
title_full_unstemmed Ring attractor bio-inspired neural network for social robot navigation
title_short Ring attractor bio-inspired neural network for social robot navigation
title_sort ring attractor bio-inspired neural network for social robot navigation
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10501606/
https://www.ncbi.nlm.nih.gov/pubmed/37719331
http://dx.doi.org/10.3389/fnbot.2023.1211570
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