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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)

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, 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.

Keywords

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

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Copyright information

© The Author(s) 2019

Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), 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.

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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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