Gait Adaptation After Leg Amputation of Hexapod Walking Robot Without Sensory Feedback

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F22%3A00364540" target="_blank" >RIV/68407700:21230/22:00364540 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1007/978-3-031-15934-3_54" target="_blank" >https://doi.org/10.1007/978-3-031-15934-3_54</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-15934-3_54" target="_blank" >10.1007/978-3-031-15934-3_54</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Gait Adaptation After Leg Amputation of Hexapod Walking Robot Without Sensory Feedback

Popis výsledku v původním jazyce

In this paper, we address the adaptation of the locomotion controller to change of the multi-legged walking robot morphology, such as leg amputation. In nature, the animal compensates for the amputation using its neural locomotion controller that we aim to reproduce with the Central Pattern Generator (CPG). The CPG is a rhythm-generating recurrent neural network used in gait controllers for the rhythmical locomotion of walking robots. The locomotion corresponds to the robot's morphology, and therefore, the locomotion rhythm must adapt if the robot's morphology is changed. The leg amputation can be handled by sensory feedback to compensate for the load distribution imbalances. However, the sensory feedback can be disrupted due to unexpected external events causing the leg to be damaged, thus leading to unexpected motion states. Therefore, we propose dynamic rules for learning a new gait rhythm without the sensory feedback input. The method has been experimentally validated on a real hexapod walking robot to demonstrate its usability for gait adaptation after amputation of one or two legs.

Název v anglickém jazyce

Gait Adaptation After Leg Amputation of Hexapod Walking Robot Without Sensory Feedback

Popis výsledku anglicky

In this paper, we address the adaptation of the locomotion controller to change of the multi-legged walking robot morphology, such as leg amputation. In nature, the animal compensates for the amputation using its neural locomotion controller that we aim to reproduce with the Central Pattern Generator (CPG). The CPG is a rhythm-generating recurrent neural network used in gait controllers for the rhythmical locomotion of walking robots. The locomotion corresponds to the robot's morphology, and therefore, the locomotion rhythm must adapt if the robot's morphology is changed. The leg amputation can be handled by sensory feedback to compensate for the load distribution imbalances. However, the sensory feedback can be disrupted due to unexpected external events causing the leg to be damaged, thus leading to unexpected motion states. Therefore, we propose dynamic rules for learning a new gait rhythm without the sensory feedback input. The method has been experimentally validated on a real hexapod walking robot to demonstrate its usability for gait adaptation after amputation of one or two legs.

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/GC21-33041J" target="_blank" >GC21-33041J: Učení plánování pohybu ve složitých úlohách</a><br>

Návaznosti

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

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název statě ve sborníku

Artificial Neural Networks and Machine Learning – ICANN 2022

ISBN

978-3-031-15933-6

ISSN

0302-9743

e-ISSN

—

Počet stran výsledku

12

Strana od-do

656-667

Název nakladatele

Springer, Cham

Místo vydání

—

Místo konání akce

Bristol

Datum konání akce

6. 9. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000866212600053