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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Rettig, O., Müller, S., Strand, M., Katic, D. (2019). Which deep artifical neural network architecture to use for anomaly detection in Mobile Robots kinematic data?. In: Beyerer, J., Kühnert, C., Niggemann, O. (eds) Machine Learning for Cyber Physical Systems. Technologien für die intelligente Automation, vol 9. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-58485-9_7
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DOI: https://doi.org/10.1007/978-3-662-58485-9_7
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