Which deep artifical neural network architecture to use for anomaly detection in Mobile Robots kinematic data?
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.
Keywordsneural networks DL4J anomaly detection inertial sensor data mobile robotics deep learning
- 2.Gamboa, J. C. B.: Deep Learning for Time-Series Analysis. arXiv preprint arXiv:1701.01887. (2017) 63Google Scholar
- 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.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)
- 7.Kingma, D. P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114. (2013)
- 8.Rezende, D. J., Mohamed, S., Wierstra, D.: Stochastic backpropagation and approximate inference in deep generative models. arXiv preprint arXiv:1401.4082. (2014)
- 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.Fabius, O., van Amersfoort, J. R.: Variational recurrent auto-encoders. arXiv preprint arXiv:1412.6581 (2014)
- 12.Graves, A.: Supervised sequence labelling with recurrent neural networks. Studies in Computational Intelligence 385 (2012)Google Scholar
- 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 (http://www.ias-15.org) 2018 (submitted and accepted)
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