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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

Projekt

<a href="/cs/project/GA18-18858S" target="_blank" >GA18-18858S: Metody kontinuálního učení řízení pohybu vícenohých kráčejích robotů v úlohách autonomního sběru dat</a><br>

Návaznosti

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

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of 2020 International Joint Conference on Neural Networks

ISBN

978-1-7281-6926-2

ISSN

—

e-ISSN

2161-4407

Počet stran výsledku

8

Strana od-do

—

Název nakladatele

IEEE Service Center

Místo vydání

Piscataway

Místo konání akce

Glasgow

Datum konání akce

19. 7. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000626021407019