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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.
Afiliación
  • Rivero-Ortega JD; Department of Engineering, Universidad Autónoma de Occidente, Cali, Colombia.
  • Mosquera-Maturana JS; Department of Engineering, Universidad Autónoma de Occidente, Cali, Colombia.
  • Pardo-Cabrera J; Department of Engineering, Universidad Autónoma de Occidente, Cali, Colombia.
  • Hurtado-López J; Department of Mathematics, Universidad Autónoma de Occidente, Cali, Colombia.
  • Hernández JD; School of Computer Science and Informatics, Cardiff University, Cardiff, United Kingdom.
  • Romero-Cano V; Robotics and Autonomous Systems Laboratory, Faculty of Engineering, Universidad Autonoma de Occidente, Cali, Colombia.
  • Ramírez-Moreno DF; Rimac Technology, Zagreb, Croatia.
Front Neurorobot ; 17: 1211570, 2023.
Article en En | MEDLINE | ID: mdl-37719331
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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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Qualitative_research Aspecto: Determinantes_sociais_saude Idioma: En Revista: Front Neurorobot Año: 2023 Tipo del documento: Article País de afiliación: Colombia Pais de publicación: Suiza
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Qualitative_research Aspecto: Determinantes_sociais_saude Idioma: En Revista: Front Neurorobot Año: 2023 Tipo del documento: Article País de afiliación: Colombia Pais de publicación: Suiza