On Unsupervised Learning of Traversal Cost and Terrain Types Identification Using Self-organizing Maps

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F19%3A00336164" target="_blank" >RIV/68407700:21230/19:00336164 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/chapter/10.1007/978-3-030-30487-4_50" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-030-30487-4_50</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-30487-4_50" target="_blank" >10.1007/978-3-030-30487-4_50</a>

Result language

angličtina

Original language name

On Unsupervised Learning of Traversal Cost and Terrain Types Identification Using Self-organizing Maps

Original language description

This paper reports on the deployment of self-organizing maps in unsupervised learning of the traversal cost for a hexapod walking robot. The problem is motivated by traversability assessment of terrains not yet visited by the robot, but for which shape and appearance features are available. The perception system of the robot is used to extract terrain features that are accompanied by traversal cost characterization captured from the real experience of the robot with the terrain, which is characterized by proprioceptive features. The learned model is employed to predict the traversal cost of new terrains based only on the shape and appearance features. Based on the experimental deployment of the robot in various terrains, a dataset of the traversal cost has been collected that is utilized in the presented evaluation of the traversal cost modeling using self-organizing map approach. In comparison with the Gaussian process, the self-organizing map provides competitive results and the found paths using the predicted traversal costs are close to the optimal path based on reference traversal cost of the particular terrain types. Besides, the self-organizing map can also be utilized for unsupervised identification of the terrain types, and it further supports incremental learning, which is more suitable for practical deployments of the robot in a priory unknown environments where reference traversal costs are not available.

Czech name

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Czech description

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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)

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)

Publication year

2019

Confidentiality

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

Article name in the collection

Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation. ICANN 2019

ISBN

978-3-030-30486-7

ISSN

0302-9743

e-ISSN

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Number of pages

15

Pages from-to

654-668

Publisher name

Springer

Place of publication

Basel

Event location

Munich

Event date

Sep 17, 2019

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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