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.
This work was funded as part of the Cluster of Excellence Cognitive Interaction Technology ‘CITEC’ (EXC 277), Bielefeld University and by the German Federal Ministry of Education and Research (BMBF) within the Leading-Edge Cluster Competition “it’s OWL” (intelligent technical systems OstWestfalenLippe) and managed by the Project Management Agency Karlsruhe (PTKA).
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Notes
- 1.
At https://doi.org/10.4119/unibi/2900912 and https://doi.org/10.4119/unibi/2900911. Detailed technical usage instructions are given there.
- 2.
Incomplete submissions include visitors which only opened the welcome page once.
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Categories were combined as it turned out to be hard to distinguish between them.
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Up to the knowledge of an expert user.
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Limited so that the target fault time fits into the slice.
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In appropriate situations. For instance, the grasping controller fault is only detectable in case the arm is used.
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Wienke, J., Meyer zu Borgsen, S., Wrede, S. (2016). A Data Set for Fault Detection Research on Component-Based Robotic Systems. In: Alboul, L., Damian, D., Aitken, J. (eds) Towards Autonomous Robotic Systems. TAROS 2016. Lecture Notes in Computer Science(), vol 9716. Springer, Cham. https://doi.org/10.1007/978-3-319-40379-3_35
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