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A Collision Avoidance Method for Autonomous Underwater Vehicles Based on Long Short-Term Memories

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Innovations in Bio-Inspired Computing and Applications (IBICA 2022)

Abstract

Over the past decades, underwater robotics has enjoyed growing popularity and relevance. While performing a mission, one crucial task for Autonomous Underwater Vehicles (AUVs) is bottom tracking, which should keep a constant distance from the seabed. Since static obstacles like walls, rocks, or shipwrecks can lie on the sea bottom, bottom tracking needs to be extended with obstacle avoidance. As AUVs face a wide range of uncertainties, implementing these essential operations is still challenging.

A simple rule-based control method has been proposed in [7] to realize obstacle avoidance. In this work, we propose an alternative AI-based control method using a Long Short-Term Memory network. We compare the performance of both methods using real-world data as well as via a simulator.

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Acknowledgements

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 956200. For more info, please visit https://remaro.eu.

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Correspondence to László Antal .

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Antal, L. et al. (2023). A Collision Avoidance Method for Autonomous Underwater Vehicles Based on Long Short-Term Memories. In: Abraham, A., Bajaj, A., Gandhi, N., Madureira, A.M., Kahraman, C. (eds) Innovations in Bio-Inspired Computing and Applications. IBICA 2022. Lecture Notes in Networks and Systems, vol 649. Springer, Cham. https://doi.org/10.1007/978-3-031-27499-2_42

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