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Guidance, Navigation, and Control of AUVs for Permanent Underwater Optical Networks

Ocean exploration is in its infancy with more than 80% of the oceans still unexplored. Contrary to space exploration, electromagnetic waves have seldom been used underwater because of their heavy absorption by seawater. In this article, we present guidance, navigation, and control systems to steer a...

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Bibliographic Details
Published in:IEEE journal of oceanic engineering 2023-01, Vol.48 (1), p.1-16
Main Authors: Immas, Alexandre, Alam, Mohammad-Reza
Format: Article
Language:English
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Summary:Ocean exploration is in its infancy with more than 80% of the oceans still unexplored. Contrary to space exploration, electromagnetic waves have seldom been used underwater because of their heavy absorption by seawater. In this article, we present guidance, navigation, and control systems to steer autonomous underwater vehicles (AUVs) such that a permanent optical network can be set up between the deep sea and the surface. The challenge is to guarantee that there is consistently a path through the AUV network to relay the signal for an unlimited time despite the finite battery's lifetime of the AUVs. To achieve this, the guidance system combines a decentralized model predictive control (D-MPC) with graph theory to compute the trajectory of each agent relaying the optical signal as well as of redundant agents forming local leader-follower formations with each relay acting as leader of its own subfleet. The D-MPC solves a constrained optimization problem where the communication and collision avoidance constraints are formulated to enable two successive leaders and one of their followers to remain within optical range and thus allowing to immediately re-route the ad hoc network if a leader fails to relay the signal. Its combination with graph theory allows to change the dynamic topology of the swarm, with additional agents diving from a charging station, such that the network can relay the optical signal for an unlimited time.
ISSN:0364-9059
1558-1691
DOI:10.1109/JOE.2022.3204328