A Bionic Spatial Cognition Model and Method for Robots Based on the Hippocampus Mechanism

Yuan, Jinsheng; Wei, Guo; Zha, Fusheng; Wang, Pengfei; Li, Mantian

doi:10.3389/fnbot.2021.769829

Cited by 1 publication

(1 citation statement)

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“…In developing effective navigation systems for robots, ring attractor models have been employed to enable effective path planning, obstacle avoidance, and goal-directed behavior (Chen and Mo, 2019 ; Robinson et al, 2022 ; Yuan et al, 2022 ). Chen and Mo ( 2019 ) employed a ring attractor model as a key component of their robotic navigation system.…”

Section: Introductionmentioning

confidence: 99%

Ring attractor bio-inspired neural network for social robot navigation

Rivero-Ortega,

Mosquera-Maturana,

Pardo-Cabrera

et al. 2023

Front. Neurorobot.

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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.

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