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

  • Johannes Wienke
  • Sebastian Meyer zu Borgsen
  • Sebastian Wrede
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9716)

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.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Johannes Wienke
    • 1
  • Sebastian Meyer zu Borgsen
    • 2
  • Sebastian Wrede
    • 1
  1. 1.Research Institute for Cognition and Robotics (CoR-Lab)Bielefeld UniversityBielefeldGermany
  2. 2.Center of Excellence Cognitive Interaction Technology (CITEC)Bielefeld UniversityBielefeldGermany

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