High-speed underwater acoustic communication for multi-agent supervised autonomy

Jarrot, Arnaud; Gelman, Andriy; Choi, Gloria; Speck, A.; Strunk, Gavin; Croux, Arnaud; Osedach, Timothy P.; Vannuffelen, Stephane; Ossia, Sepand; Vincent, Jack; Grall, Sebastien; Eudeline, Guillaume

doi:10.1109/ucomms50339.2021.9598036

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…Last, the tracking surface vehicle could also be used as a communication hub to enable human-in-the-loop operations and to relay information (for example, imagery) from the underwater asset during prescribed missions. This supervised autonomy approach could be used to extend the duration of missions, enable cooperation between shoreside experts, and allow for mission changes and corrections based on human input, thereby offering other ways to study the ocean (12,47,48). In such scenarios, seamless multivehicle coordination and collaboration with human operators become crucial.…”

Section: Discussionmentioning

confidence: 99%

Dynamic robotic tracking of underwater targets using reinforcement learning

et al. 2023

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To realize the potential of autonomous underwater robots that scale up our observational capacity in the ocean, new techniques are needed. Fleets of autonomous robots could be used to study complex marine systems and animals with either new imaging configurations or by tracking tagged animals to study their behavior. These activities can then inform and create new policies for community conservation. The role of animal connectivity via active movement of animals represents a major knowledge gap related to the distribution of deep ocean populations. Tracking underwater targets represents a major challenge for observing biological processes in situ, and methods to robustly respond to a changing environment during monitoring missions are needed. Analytical techniques for optimal sensor placement and path planning to locate underwater targets are not straightforward in such cases. The aim of this study was to investigate the use of reinforcement learning as a tool for range-only underwater target-tracking optimization, whose promising capabilities have been demonstrated in terrestrial scenarios. To evaluate its usefulness, a reinforcement learning method was implemented as a path planning system for an autonomous surface vehicle while tracking an underwater mobile target. A complete description of an open-source model, performance metrics in simulated environments, and evaluated algorithms based on more than 15 hours of at-sea field experiments are presented. These efforts demonstrate that deep reinforcement learning is a powerful approach that enhances the abilities of autonomous robots in the ocean and encourages the deployment of algorithms like these for monitoring marine biological systems in the future.

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Section: Discussionmentioning

confidence: 99%

Dynamic robotic tracking of underwater targets using reinforcement learning

et al. 2023

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“…In communication systems using SC signals, a method combining a decision feedback equalizer (DFE) and digital phase locked loop (DPLL) has been widely employed. [8][9][10][11][12][13][14][15] The DFE-DPLL approach has been applied in various environments, including ultra-long-distance horizontal communication exceeding 100 km, 8) high-speed communication between deepsea exploration instruments and offshore equipment, [9][10][11] shortrange high-speed communication in shallow water areas, 12) and horizontal mobile communication in shallow water areas. 13) In recent years, DFE methods have been devised to address the challenges of highly dynamic multipath interference, including detecting and correcting the phase shift of each multipath wave 14) and employing iterative equalization processes using posterior probability distributions based on error correction.…”

Section: Introductionmentioning

confidence: 99%

“…15) The communication system using SC and DFE enables efficient construction of communication signal frames, allowing for relatively high-speed communication even in real maritime environments. [9][10][11][12] On the other hand, when dealing with signals characterized by large temporal spread and sparse responses, DFE schemes require surplus filter taps, leading to issues such as increased computational costs, complexity in filter coefficient estimation, and the impact of noise from surplus taps. Furthermore, DFE is known to generate burst errors due to error propagation affecting subsequent signals.…”

Section: Introductionmentioning

confidence: 99%

A sparse adaptive decision feedback equalization approach for the high data rate vertical underwater acoustic communication in deep sea

Kida,

Deguchi,

Shimura

2024

Jpn. J. Appl. Phys.

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In underwater acoustic communication environments, multichannel decision feedback equalizer (MDFE) techniques combined with single carrier modulation schemes are widely employed to suppress multipath interference while adapting to time-varying channel response. While MDFE is a powerful technique, the estimation of the numerous filter taps is generally complex problem, and the numerically complexities can lead to the instability of solutions including the occurrence of the burst errors. In this study, we propose a method that introduces L1-norm regularization into the adaptive algorithm of MDFE, considering a sparse channel response environment in the vertical underwater acoustic communication channel in deep-sea areas. We applied the proposed method to simulation and experimental data, comparing it with conventional methods. As a result, the proposed method demonstrated a clear improvement in BER performance, obtaining sparse filter coefficient estimates and effectively suppressing burst errors.

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Construction and application of polar codes in OFDM underwater acoustic communication

Chen

Zeng

et al. 2023

Applied Acoustics

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High-speed underwater acoustic communication for multi-agent supervised autonomy

Cited by 5 publications

References 7 publications

Dynamic robotic tracking of underwater targets using reinforcement learning

Dynamic robotic tracking of underwater targets using reinforcement learning

A sparse adaptive decision feedback equalization approach for the high data rate vertical underwater acoustic communication in deep sea

Construction and application of polar codes in OFDM underwater acoustic communication

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