Terrain Traversal Cost Learning with Knowledge Transfer Between Multi-legged Walking Robot Gaits
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F22%3A00359487" target="_blank" >RIV/68407700:21230/22:00359487 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/ICARSC55462.2022.9784790" target="_blank" >https://doi.org/10.1109/ICARSC55462.2022.9784790</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICARSC55462.2022.9784790" target="_blank" >10.1109/ICARSC55462.2022.9784790</a>
Alternative languages
Result language
angličtina
Original language name
Terrain Traversal Cost Learning with Knowledge Transfer Between Multi-legged Walking Robot Gaits
Original language description
The terrain traversal abilities of multi-legged walking robots are affected by gaits, the walking patterns that enable adaptation to various operational environments. Fast and lowset gaits are suited to flat ground, while cautious and highset gaits enable traversing rough areas. A suitable gait can be selected using prior experience with a particular terrain type. However, experience alone is insufficient in practical setups, where the robot experiences each terrain with only one or just a few gaits and thus would infer novel gait-terrain interactions from insufficient data. Therefore, we use knowledge transfer to address unsampled gait-terrain interactions and infer the traversal cost for every gait. The proposed solution combines gaitterrain cost models using inferred gait-to-gait models projecting the robot experiences between different gaits. We implement the cost models as Gaussian Mixture regressors providing certainty to identify unknown terrains where knowledge transfer is desirable. The presented method has been verified in synthetic showcase scenarios and deployment with a real walking robot. The proposed knowledge transfer demonstrates improved cost prediction and selection of the appropriate gait for specific terrains.
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
Result was created during the realization of more than one project. More information in the Projects tab.
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
2022 IEEE INTERNATIONAL CONFERENCE ON AUTONOMOUS ROBOT SYSTEMS AND COMPETITIONS (ICARSC)
ISBN
978-1-6654-8217-2
ISSN
2573-9360
e-ISSN
2573-9387
Number of pages
6
Pages from-to
148-153
Publisher name
Institute of Electrical and Electronics Engineers
Place of publication
New York
Event location
Santa Maria da Feira
Event date
Apr 29, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000838705300026