Which deep artifical neural network architecture to use for anomaly detection in Mobile Robots kinematic data?

  • Oliver RettigEmail author
  • Silvan Müller
  • Marcus Strand
  • Darko Katic
Open Access
Conference paper
Part of the Technologien für die intelligente Automation book series (TIA, volume 9)


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, kinematics data is collected exemplarily 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.


neural networks DL4J anomaly detection inertial sensor data mobile robotics deep learning 


  1. 1.
    Meyer J., Filliat D.: Map-based navigation in mobile robots: II A review of maplearning and path-planning strategies. Cognitive Systems Research 4 283-317 (2003)CrossRefGoogle Scholar
  2. 2.
    Gamboa, J. C. B.: Deep Learning for Time-Series Analysis. arXiv preprint arXiv:1701.01887. (2017) 63Google Scholar
  3. 3.
    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)
  4. 4.
    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)
  5. 5.
    Neto, H. V., Nehmzow, U.: Real-time automated visual inspection using mobile robots. Journal of Intelligent and Robotic Systems, 49(3), 293-307 (2007)CrossRefGoogle Scholar
  6. 6.
    Sofman, B., Neuman, B., Stentz, A., Bagnell, J. A.: Anytime online novelty and change detection for mobile robots. Journal of Field Robotics, 28(4), 589-618 (2011)CrossRefGoogle Scholar
  7. 7.
    Kingma, D. P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114. (2013)
  8. 8.
    Rezende, D. J., Mohamed, S., Wierstra, D.: Stochastic backpropagation and approximate inference in deep generative models. arXiv preprint arXiv:1401.4082. (2014)
  9. 9.
    Sölch, M., Bayer, J., Ludersdorfer, M., van der Smagt, P.: Variational inference for online anomaly detection in high-dimensional timeseries. arXiv preprint arXiv:1602.07109. (2016)
  10. 10.
    Fabius, O., van Amersfoort, J. R.: Variational recurrent auto-encoders. arXiv preprint arXiv:1412.6581 (2014)
  11. 11.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation, 9(8), 1735-1780 (1997)CrossRefGoogle Scholar
  12. 12.
    Graves, A.: Supervised sequence labelling with recurrent neural networks. Studies in Computational Intelligence 385 (2012)Google Scholar
  13. 13.
    Rettig, O., Mller, S., Strand, M., Katic, D.: Unsupervised Hump Detection for Mobile Robots Based on Kinematic Measurements and Deep-Learning Based Autoencoder. IAS-15 ( 2018 (submitted and accepted)

Copyright information

© The Author(s) 2019

Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

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

Personalised recommendations