Terrain Learning Using Time Series of Ground Unit Traversal Cost
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F20%3A00342303" target="_blank" >RIV/68407700:21230/20:00342303 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/978-3-030-43890-6_8" target="_blank" >https://doi.org/10.1007/978-3-030-43890-6_8</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-43890-6_8" target="_blank" >10.1007/978-3-030-43890-6_8</a>
Alternative languages
Result language
angličtina
Original language name
Terrain Learning Using Time Series of Ground Unit Traversal Cost
Original language description
In this paper, we concern learning of terrain types based on the traversal experience observed by a hexapod walking robot. The addressed problem is motivated by the navigation of unmanned ground vehicles in long-term autonomous missions in a priory unknown environments such as extraterrestrial exploration. In such deployments, the robotic vehicle needs to learn hard to traverse terrains to improve its autonomous performance and avoid possibly dangerous areas. We propose to utilize Growing Neural Gas for terrain learning to capture the robot experience with traversing the terrain and thus learn a classifier of individual terrain types. The classifier is learned using a real time-series dataset collected by a hexapod walking robot traversing various terrain types. The learned model can be utilized to predict the traversal cost of newly observed terrains to support decisions on where to navigate next.
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/EF16_019%2F0000765" target="_blank" >EF16_019/0000765: Research Center for Informatics</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
6th International Workshop on Modelling and Simulation for Autonomous Systems
ISBN
9783030438890
ISSN
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e-ISSN
1611-3349
Number of pages
11
Pages from-to
97-107
Publisher name
Springer
Place of publication
Wien
Event location
Palermo
Event date
Oct 29, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000628847000008