Using Machine Learning to Identify Activities of a Flying Drone from Sensor Readings
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F17%3A10371744" target="_blank" >RIV/00216208:11320/17:10371744 - isvavai.cz</a>
Result on the web
<a href="https://aaai.org/ocs/index.php/FLAIRS/FLAIRS17/paper/view/15488/14980" target="_blank" >https://aaai.org/ocs/index.php/FLAIRS/FLAIRS17/paper/view/15488/14980</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Using Machine Learning to Identify Activities of a Flying Drone from Sensor Readings
Original language description
The dawn of autonomous robots brings a question of automated modeling of robot behavior such that the learned robot capabilities can be used to plan robot activities. To bridge the continuous world of sensor readings and control signals with the symbolic world of planning, one needs to identify robot activities as somehow compact behaviors that can be repeated later when a given activity is planned to be performed. In this paper we focus on identifying activities from a sequence of sensor reading and corresponding control signals by using the methods of machine learning, both supervised and unsupervised. The methods are experimentally evaluated using data from a flying drone.
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/GA15-19877S" target="_blank" >GA15-19877S: Automated Knowledge and Plan Modeling for Autonomous Robots</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the Thirtieth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2017
ISBN
978-1-57735-787-2
ISSN
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e-ISSN
neuvedeno
Number of pages
6
Pages from-to
436-441
Publisher name
AAAI Press
Place of publication
USA
Event location
Marco Island, Florida, USA
Event date
May 22, 2017
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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