Deep Transfer Learning of Traversability Assessment for Heterogeneous Robots

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F22%3A00364546" target="_blank" >RIV/68407700:21230/22:00364546 - isvavai.cz</a>

Result on the web

<a href="https://ceur-ws.org/Vol-3226/paper1.pdf" target="_blank" >https://ceur-ws.org/Vol-3226/paper1.pdf</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Deep Transfer Learning of Traversability Assessment for Heterogeneous Robots

Original language description

For autonomous robots operating in an unknown environment, it is important to assess the traversability of the surrounding terrain to improve path planning and decision-making on where to navigate next in a cost-efficient way. Specifically, in mobile robot exploration, terrains and their traversability are unknown prior to the deployment. The robot needs to use its limited resources to learn its terrain traversability model on the go; however, reusing a provided model is still a desirable option. In a team of heterogeneous robots, the models assessing traversability cannot be reused directly since robots might possess different morphology or sensory equipment and thus experience the terrain differently. In this paper, we propose a transfer learning approach for convolutional neural networks assessing the traversability between heterogeneous robots, where the transferred network is retrained using data available for the target robot to accommodate itself to the robot’s traversability. The proposed method is verified in real-world experiments, where the proposed approach provides faster learning convergence and better traversal cost predictions than the baseline.

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

<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)

Publication year

2022

Confidentiality

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

Article name in the collection

Proceedings of the 22nd Conference Information Technologies – Applications and Theory (ITAT 2022)

ISBN

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ISSN

1613-0073

e-ISSN

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Number of pages

9

Pages from-to

12-20

Publisher name

CEUR Workshop Proceedings

Place of publication

Aachen

Event location

Zuberec

Event date

Sep 23, 2022

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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