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Single-Class Discrimination for Robot Anomaly Detection

Hornung, Rachel (2012) Single-Class Discrimination for Robot Anomaly Detection. Master's, Technische Universität München.

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Abstract

Early system error detection is essential for safe human-robot interaction. Detecting errors in the large amount of sensory and status data is, however, difficult, especially since the amount of fault data is very small if at all available; a problem for which no sufficiently stable methods exist. This thesis introduces a new Robot Anomaly Detection System (RADS) consisting of a multi-stage solution, that adapts to high dimensional input and detects errors without previous records of them. Application to real robot data proves its satisfying performance.

Item URL in elib:https://elib.dlr.de/80401/
Document Type:Thesis (Master's)
Title:Single-Class Discrimination for Robot Anomaly Detection
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Hornung, RachelUNSPECIFIEDUNSPECIFIED
Date:October 2012
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Number of Pages:44
Status:Published
Keywords:Single-Class Discrimination, Anomaly Detection, One-Class Discrimination, Robot Anomaly Detection
Institution:Technische Universität München
Department:Fakultät für Informatik
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space Technology
DLR - Research area:Raumfahrt
DLR - Program:R SY - Technik für Raumfahrtsysteme
DLR - Research theme (Project):R - RMC - Kognitive Intelligenz und Autonomie (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Robotics and Mechatronics (until 2012)
Deposited By: Urbanek, Holger
Deposited On:11 Jan 2013 14:54
Last Modified:31 Jul 2019 19:39

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