Neurodynamic Sensory-Motor Phase Binding for Multi-Legged Walking Robots

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F20%3A00346401" target="_blank" >RIV/68407700:21230/20:00346401 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1109/IJCNN48605.2020.9207507" target="_blank" >https://doi.org/10.1109/IJCNN48605.2020.9207507</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/IJCNN48605.2020.9207507" target="_blank" >10.1109/IJCNN48605.2020.9207507</a>

Result language

angličtina

Original language name

Neurodynamic Sensory-Motor Phase Binding for Multi-Legged Walking Robots

Original language description

Motivated by observations of animal behavior, locomotion of multi-legged walking robots can be controlled by the central pattern generators (CPGs) that produce a repetitive motion pattern. A rhythmic pattern, a gait, is defined by phase relations between all leg joints. In a case of an external influence such as terrain irregularity, some actuator phase can shift and thus disrupt the phase relations between the actuators. The actuator phase relations can be maintained only by synchronizing to the sensors, which output can indicate the motion disruption. However, establishing correct sensory-motor phase relations requires not only the motor phase model but also a model of the sensory phase, which is generally unknown. Although both sensory and motor phases can be modeled by single CPG, the capabilities of such CPG-based controllers are limited because they are not flexible and robust. In this paper, we propose to model the phases of each sensor and motor by separate CPGs. The phase relations between the sensor and motor phases are established by radial basis function (RBF) neurons learned with proposed periodic Grossberg rule for which we present the convergence proof. Based on the reported evaluation results using high-fidelity simulation, the proposed locomotion controller demonstrates the desired plasticity, and it is capable of learning multiple gaits with robust synchronization to terrain changes using sensor inputs.

Czech name

—

Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GA18-18858S" target="_blank" >GA18-18858S: Robotic Lifelong Learning of Multi-legged Robot Locomotion Control in Autonomous Data Collection Missions</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2020

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings of 2020 International Joint Conference on Neural Networks

ISBN

978-1-7281-6926-2

ISSN

—

e-ISSN

2161-4407

Number of pages

8

Pages from-to

—

Publisher name

IEEE Service Center

Place of publication

Piscataway

Event location

Glasgow

Event date

Jul 19, 2020

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000626021407019