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Learning of Multi-Context Models for Autonomous Underwater Vehicles

Bilal Wehbe; Luis Octavio Arriaga Camargo; Mario Michael Krell; Frank Kirchner
In: Proceedings of the AUV 2018 IEEE/OES Porto. IEEE/OES Autonomous Underwater Vehicles (AUV-2018), November 6-9, Porto, Portugal, IEEE, 11/2018.


Multi-context model learning is crucial for marine robotics where several factors can cause disturbances to the system’s dynamics. This work addresses the problem of identifying multiple contexts of an AUV model. We build a simulation model of the robot from experimental data, and use it to fill in the missing data and generate different model contexts. We implement an architecture based on long-short-term-memory (LSTM) networks to learn the different contexts directly from the data. We show that the LSTM network can achieve high classification accuracy compared to baseline methods, showing robustness against noise and scaling efficiently on large datasets.


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