A Data Set for Fault Detection Research on Component-Based Robotic Systems

Wienke J, Meyer zu Borgsen S, Wrede S (2016)
In: Towards Autonomous Robotic Systems. Alboul L, Damian D, Aitken JM (Eds); Lecture Notes in Artificial Intelligence, 9716. Springer International Publishing: 339-350.

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Conference Paper | Published | English
Editor
Alboul, Lyuba ; Damian, Dana ; Aitken, Jonathan M.
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.
Publishing Year
Conference
17th Towards Autonomous Robotic Systems (TAROS-16)
Location
Sheffield, UK
Conference Date
2016-06-26 – 2016-07-01
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Wienke J, Meyer zu Borgsen S, Wrede S. A Data Set for Fault Detection Research on Component-Based Robotic Systems. In: Alboul L, Damian D, Aitken JM, eds. Towards Autonomous Robotic Systems. Lecture Notes in Artificial Intelligence. Vol 9716. Springer International Publishing; 2016: 339-350.
Wienke, J., Meyer zu Borgsen, S., & Wrede, S. (2016). A Data Set for Fault Detection Research on Component-Based Robotic Systems. In L. Alboul, D. Damian, & J. M. Aitken (Eds.), Lecture Notes in Artificial Intelligence: Vol. 9716. Towards Autonomous Robotic Systems (pp. 339-350). Springer International Publishing.
Wienke, J., Meyer zu Borgsen, S., and Wrede, S. (2016). “A Data Set for Fault Detection Research on Component-Based Robotic Systems” in Towards Autonomous Robotic Systems, Alboul, L., Damian, D., and Aitken, J. M. eds. Lecture Notes in Artificial Intelligence, vol. 9716, (Springer International Publishing), 339-350.
Wienke, J., Meyer zu Borgsen, S., & Wrede, S., 2016. A Data Set for Fault Detection Research on Component-Based Robotic Systems. In L. Alboul, D. Damian, & J. M. Aitken, eds. Towards Autonomous Robotic Systems. Lecture Notes in Artificial Intelligence. no.9716 Springer International Publishing, pp. 339-350.
J. Wienke, S. Meyer zu Borgsen, and S. Wrede, “A Data Set for Fault Detection Research on Component-Based Robotic Systems”, Towards Autonomous Robotic Systems, L. Alboul, D. Damian, and J.M. Aitken, eds., Lecture Notes in Artificial Intelligence, vol. 9716, Springer International Publishing, 2016, pp.339-350.
Wienke, J., Meyer zu Borgsen, S., Wrede, S.: A Data Set for Fault Detection Research on Component-Based Robotic Systems. In: Alboul, L., Damian, D., and Aitken, J.M. (eds.) Towards Autonomous Robotic Systems. Lecture Notes in Artificial Intelligence. 9716, p. 339-350. Springer International Publishing (2016).
Wienke, Johannes, Meyer zu Borgsen, Sebastian, and Wrede, Sebastian. “A Data Set for Fault Detection Research on Component-Based Robotic Systems”. Towards Autonomous Robotic Systems. Ed. Lyuba Alboul, Dana Damian, and Jonathan M. Aitken. Springer International Publishing, 2016.Vol. 9716. Lecture Notes in Artificial Intelligence. 339-350.
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