Paper Info
Title
A Data Set for Fault Detection Research on Component-Based Robotic Systems.
Abstract
Fault detection and identification methods (FDI) are an important aspect for ensuring consistent behavior of technical systems. In robotics FDI promises to improve the autonomy and robustness. Existing FDI research in robotics mostly focused on faults in specific areas, like sensor faults. While there is FDI research also on the overarching software system, common data sets to benchmark such solutions do not exist. In this paper we present a data set for FDI research on robot software systems to bridge this gap. We have recorded an HRI scenario with our RoboCup@Home platform and induced diverse empirically grounded faults using a novel, structured method. The recordings include the complete event-based communication of the system as well as detailed performance counters for all system components and exact ground-truth information on the induced faults. The resulting data set is a challenging benchmark for FDI research in robotics which is publicly available.
Year
DOI
Venue
2016
10.1007/978-3-319-40379-3_35
Lecture Notes in Artificial Intelligence
Field
DocType
Volume
Robotic systems,Data set,Computer science,Fault detection and isolation,Real-time computing,Software system,Robustness (computer science),Artificial intelligence,Robot software,Technical systems,Robotics
Conference
9716
ISSN
Citations 
PageRank 
0302-9743
2
0.38
References 
Authors
4
3
Name
Order
Citations
PageRank
Johannes Wienke1294.09
Sebastian Meyer Zu Borgsen2184.01
Sebastian Wrede313920.59