Gait Adaptation After Leg Amputation of Hexapod Walking Robot Without Sensory Feedback
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Gait Adaptation After Leg Amputation of Hexapod Walking Robot Without Sensory Feedback
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GC21-33041J" target="_blank" >GC21-33041J: Learning Complex Motion Planning Policies</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Artificial Neural Networks and Machine Learning – ICANN 2022
ISBN
978-3-031-15933-6
ISSN
0302-9743
e-ISSN
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Number of pages
12
Pages from-to
656-667
Publisher name
Springer, Cham
Place of publication
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Event location
Bristol
Event date
Sep 6, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000866212600053