Ring attractor bio-inspired neural network for robot social navigation

Frontiers in Neurorobotics

Neuroscience

Juan S. Mosquera-Maturana¹  Josh Pardo-Cabrera¹  Jesús D. Rivero-Ortega¹  Julián Hurtado-López²  David F. Ramírez-Moreno³ 

[1] Department of Engineering, Universidad Autónoma de Occidente, Cali, Colombia;Department of Mathematics, Universidad Autónoma de Occidente, Cali, Colombia;Department of Physics, Universidad Autónoma de Occidente, Cali, Colombia;

关键词: bio-inspired navigation;    robot guidance;    obstacle avoidance;    decision-making;    motor control;    ring attractor networks;    social navigation;   

DOI  :  10.3389/fnbot.2023.1211570

 received in 2023-04-24, accepted in 2023-08-14,  发布年份 2023

来源: Frontiers

【 摘 要 】

IntroductionWe 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.MethodsThe 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.ResultsThe 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.DiscussionOur 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.

【 授权许可】

Unknown   
Copyright © 2023 Rivero-Ortega, Mosquera-Maturana, Pardo-Cabrera, Hurtado-López and Ramírez-Moreno.