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Unsupervised Hump Detection for Mobile Robots Based On Kinematic Measurements and Deep-Learning Based Autoencoder

  • Oliver RettigEmail author
  • Silvan Müller
  • Marcus Strand
  • Darko Katic
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 867)

Abstract

Small humps on the floor go beyond the detectable scope of laser scanners and are therefore not integrated into SLAM based maps of mobile robots. However, even such small irregularities can have a tremendous effect on the robot’s stability and the path quality. As a basis to develop anomaly detection algorithms, example kinematics data is collected for an overrun of a cable channel and a bulb plate. A recurrent neuronal network (RNN), based on the autoencoder principle, could be trained successfully with this data. The described RNN architecture looks promising to be used for realtime anomaly detection and also to quantify path quality.

Keywords

Neural networks Anomaly detection Path planning Kinematic measurement Mobile robotics Deep learning 

Notes

Acknowledgements

The kinematic measurements were executed in the Gait- and Motionlab Heidelberg, University Clinics and were supported by the local laboratory team. The project is funded by the Federal Ministry for Economic Affairs and Energy of Germany.

References

  1. 1.
    Meyer, J., Filliat, D.: Map-based navigation in mobile robots: II A review of map-learning and path-planning strategies. Cogn. Syst. Res. 4, 283–317 (2003)CrossRefGoogle Scholar
  2. 2.
    Hodge, V., Austin, J.: A survey of outlier detection methodologies. Artif. Intell. Rev. 22(2), 85–126 (2004)CrossRefGoogle Scholar
  3. 3.
    Edgeworth, F.Y.: On observations relating to several quantities. Hermathena 13(6), 279–285 (1887)Google Scholar
  4. 4.
    Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. (CSUR) 41(3), 15 (2009)CrossRefGoogle Scholar
  5. 5.
    Gamboa, J.C.B.: Deep Learning for Time-Series Analysis. arXiv preprint arXiv:1701.01887. (2017)
  6. 6.
    Tai, L., Liu, M.: Deep-learning in mobile robotics-from perception to control systems: A survey on why and why not. arXiv:1612.07139. (2016)
  7. 7.
    Neto, H.V., Nehmzow, U.: Real-time automated visual inspection using mobile robots. J. Intell. Rob. Syst. 49(3), 293–307 (2007)CrossRefGoogle Scholar
  8. 8.
    Sofman, B., Neuman, B., Stentz, A., Bagnell, J.A.: Anytime online novelty and change detection for mobile robots. J. Field Robot. 28(4), 589–618 (2011)CrossRefGoogle Scholar
  9. 9.
    Malhotra, P., Ramakrishnan, A., Anand, G., Vig, L., Agarwal, P., Shroff, G.: LSTM-based encoder-decoder for multi-sensor anomaly detection. arXiv preprint arXiv:1607.00148. (2016)
  10. 10.
    Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114. (2013)
  11. 11.
    Rezende, D.J., Mohamed, S., Wierstra, D.: Stochastic backpropagation and approximate inference in deep generative models. arXiv preprint arXiv:1401.4082. (2014)
  12. 12.
    Sölch, M., Bayer, J., Ludersdorfer, M., van der Smagt, P.: Variational inference for on-line anomaly detection in high-dimensional timeseries. arXiv preprint arXiv:1602.07109. (2016)
  13. 13.
    An, J., Cho, S.: Variational autoencoder based anomaly detection using reconstruction probability. Technical report, SNU Data Mining Center (2015)Google Scholar
  14. 14.
    Fabius, O., van Amersfoort, J.R.: Variational recurrent auto-encoders. arXiv preprint arXiv:1412.6581 (2014)
  15. 15.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRefGoogle Scholar
  16. 16.
    Graves, A.: Supervised Sequence Labelling With Recurrent Neural Networks. Studies in Computational Intelligence, vol. 385. Springer, Heidelberg (2012)zbMATHGoogle Scholar
  17. 17.
    Bengio, Y.: Practical recommendations for gradient-based training of deep architectures. In: Neural Networks: Tricks of the Trade, pp. 437-478. Springer, Berlin, Heidelberg (2012)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Oliver Rettig
    • 1
    Email author
  • Silvan Müller
    • 1
  • Marcus Strand
    • 1
  • Darko Katic
    • 2
  1. 1.Department for Computer ScienceBaden-Wuerttemberg Cooperative State UniversityKarlsruheGermany
  2. 2.ArtiMinds Robotics GmbHKarlsruheGermany

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