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

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


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.


  1. 1.
    van Beek, L., et al.: RoboCup@Home 2015: Rule and Regulations (2015).
  2. 2.
    Downs, J.J., Vogel, E.F.: A plant-wide industrial process control problem. Comput. Chem. Eng. 17(3), 245–255 (1993)CrossRefGoogle Scholar
  3. 3.
    Golombek, R., et al.: Online data-driven fault detection for robotic systems. In: Intelligent Robots and Systems, pp. 3011–3016. IEEE, San Francisco (2011)Google Scholar
  4. 4.
    Gonzalez-Bañales, D.L., Adam, M.R: Web survey design, implementation: best practices for empirical research. In: European and Mediterranean Conference on Information Systems, Valencia, Spain (2007)Google Scholar
  5. 5.
    Gunawi, H.S., et al.: What bugs live in the cloud?: a study of 3000+ issues in cloud systems. In: Proceedings of the ACM Symposium on Cloud Computing, pp. 1–14. ACM (2014)Google Scholar
  6. 6.
    Jiang, H., Elbaum, S., Detweiler, C.: Reducing failure rates of robotic systems though inferred invariants monitoring. In: Intelligent Robots and Systems, pp. 1899–1906. IEEE (November 2013)Google Scholar
  7. 7.
    Jin, G., et al.: Understanding and detecting real-world performance bugs. ACM SIGPLAN Not. 47(6), 77–88 (2012)Google Scholar
  8. 8.
    McConnell, S.: Code Complete, 2nd. Microsoft Press (2004)Google Scholar
  9. 9.
    Meyer zu Borgsen, S., et al.: ToBI-Team of Bielefeld: The Human-Robot Interaction System for RoboCup@ Home 2015 (2015)Google Scholar
  10. 10.
    Peischl, B., Weber, J., Wotawa, F.: Runtime fault detection, localization in component-oriented software systems. In: 17th International Workshop on Principles of Diagnosis (DX 2006), pp. 195–203, Penaranda de Duero, Spain (2006)Google Scholar
  11. 11.
    Pettersson, O.: Execution monitoring in robotics: a survey. Robot. Auton. Syst. 53(2), 73–88 (2005)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Siepmann, F., Wachsmuth, S.: A Modeling Framework for Reusable Social Behavior. In: Silva, R.D., Reidsma, D (ed.) Work-in-Progress Workshop Proceedings, pp. 93–96. Springer, Amsterdam (2011)Google Scholar
  13. 13.
    Steinbauer, G.: A survey about faults of robots used in robocup. In: Chen, X., Stone, P., Sucar, L.E., van der Zant, T. (eds.) RoboCup 2012. LNCS, vol. 7500, pp. 344–355. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  14. 14.
    Steinbauer, G., Wotawa, F.: Detecting and locating faults in the control software of autonomous mobile robots. In: Kaelbling, L.P. (ed.) International Joint Conference on AI, pp. 1742–1743 (2005)Google Scholar
  15. 15.
    Sydor, M.J.: APM Best Practices: Realizing Application Performance Manage- ment. Apress, New York (2010)Google Scholar
  16. 16.
    Wienke, J., Klotz, D., Wrede, S.: A framework for the acquisition of multimodal human-robot interaction data sets with a whole-system perspective. In: Multimodal Corpora: How Should Multimodal Corpora Deal with the Situation? Workshop Programme (2012)Google Scholar
  17. 17.
    Wienke, J., Wrede, S.: A middleware for collaborative research in experimental robotics. In: 2011 IEEE/SICE International Symposium on System Integration (SII), pp. 1183–1190. IEEE, Kyoto (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Johannes Wienke
    • 1
    Email author
  • 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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