A swarm of unmanned vehicles in the shallow ocean: A survey

Liu, Gaoxiang; Chen, Lei; Liu, Kexin; Luo, Ying

doi:10.1016/j.neucom.2023.02.020

Cited by 19 publications

(3 citation statements)

References 81 publications

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“…Today, the use of mobile nodes such as autonomous underwater vehicles (AUVs) for underwater exploration and cluster searches is increasing [ 1 , 2 , 3 ]. To effectively coordinate AUVs involved in underwater tasks and transmit collected data to the surface station, an underwater wireless communication network is established by employing floating structures such as buoys [ 4 , 5 , 6 ]. Acoustic communication, preferred for its reliability in transmitting data over longer distances (ranging from hundreds of meters to tens of kilometers), is the main method employed, as opposed to radio frequency (RF) and optical communications [ 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Effect of Base Station Motion on Underwater Handovers for Base-Station-Based Underwater Wireless Acoustic Networks

Yun,

Kwon

2024

Sensors

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In base-station-based underwater wireless acoustic networks (B-UWANs), effective handover mechanisms are necessary to ensure seamless data services for mobile nodes such as autonomous underwater vehicles (AUVs). Unlike terrestrial base stations (BSs), moored buoy BSs in B-UWANs experience motion responses due to wave loads under environmental conditions, posing unique challenges to the handover process. This study examines how BS motion affects handover decision errors, which arise when AUVs incorrectly initiate handovers to unintended BSs due to BS motion. By utilizing the AUV–BS distance as a handover triggering parameter, our analysis reveals a significant increase in decision errors within the overlapping regions when both the current and target BSs are in motion, especially when moving in the same direction. In addition, these errors intensify with the magnitude of BS motion and are exacerbated by smaller BS network radii. Based on these simulation results, we present an analytical framework that not only measures the influence of BS motion on the AUV–BS distance but also provides strategic insights for refining underwater handover protocols, thereby enhancing operational reliability and service continuity in B-UWANs.

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Section: Introductionmentioning

confidence: 99%

Analysis of the Effect of Base Station Motion on Underwater Handovers for Base-Station-Based Underwater Wireless Acoustic Networks

Yun,

Kwon

2024

Sensors

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“…Management methodologies employed for SDAs have been successfully utilized for allocating spaces to various moving vehicles [14]. These methodologies have found application across Unmanned Underwater Systems (UUS) [15], Unmanned Aerial Systems (UAS) [16], Byzantine robots [17], and unmanned cars [18], and their remarkable ability to ensure safety in dynamic environments has been revealed.…”

Section: Introductionmentioning

confidence: 99%

Blockchain PoS and PoW Consensus Algorithms for Airspace Management Application to the UAS-S4 Ehécatl

Hashemi,

Botez,

Ghazi

2023

Algorithms

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This paper introduces an innovative consensus algorithm for managing Unmanned Aircraft System Traffic (UTM) through blockchain technology, a highly secure consensus protocol, to allocate airspace. A smart contract was developed on the Ethereum blockchain for allocating airspace. This technique enables the division of the swarm flight zone into smaller sectors to decrease the computational complexity of the algorithm. A decentralized voting system was established within these segmented flight zones, utilizing two primary methodologies: Proof of Work (PoW) and Proof of Stake (PoS). By employing 1000 UAS-S4s across various locations and heading angles, a swarm flight zone was generated. The efficiency of the devised decentralized consensus system was assessed based on error rate and validation time. Despite PoS displaying greater efficiency in cumulative probability for block execution, the comparative analysis indicated PoW outperformed PoS concerning the potential for conflicts among UASs.

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“…Compared with the operational capability of a single AUV, collaborative AUVs can respond more reliably and flexibly to complex missions and extended operational ranges, thereby improving the efficiency and robustness of undersea operations. Given this backdrop, numerous application cases about AUV coordinated formation have been triggered in both civilian and industrial fields for decades [2,3]. Irrespective of the specific collaborative missions undertaken by AUVs, the core challenge lies in ensuring motion stability of AUV formations within complex underwater environments and the constraints of their own models.…”

Section: Introductionmentioning

confidence: 99%

Distributed Dual Closed-Loop Model Predictive Formation Control for Collision-Free Multi-AUV System Subject to Compound Disturbances

Zhang,

Yan,

Zhou

et al. 2023

JMSE

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This paper focuses on the collision-free formation tracking of autonomous underwater vehicles (AUVs) with compound disturbances in complex ocean environments. We propose a novel finite-time extended state observer (FTESO)-based distributed dual closed-loop model predictive control scheme. Initially, a fast FTESO is designed to accurately estimate both model uncertainties and external disturbances for each subsystem. Subsequently, the outer-loop and inner-loop formation controllers are developed by integrating disturbance compensation with distributed model predictive control (DMPC) theory. With full consideration of the input and state constraints, we resolve the local information-based DMPC optimization problem to obtain the control inputs for each AUV, thereby preventing actuator saturation and collisions among AUVs. Moreover, to mitigate the increased computation caused by the control structure, the Laguerre orthogonal function is applied to alleviate the computational burden in time intervals. We also demonstrate the stability of the closed-loop system by applying the terminal state constraint. Finally, based on a connected directed topology, comparative simulations are performed under various control schemes to verify the robustness and superior performance of the proposed scheme.

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A swarm of unmanned vehicles in the shallow ocean: A survey

Cited by 19 publications

References 81 publications

Analysis of the Effect of Base Station Motion on Underwater Handovers for Base-Station-Based Underwater Wireless Acoustic Networks

Analysis of the Effect of Base Station Motion on Underwater Handovers for Base-Station-Based Underwater Wireless Acoustic Networks

Blockchain PoS and PoW Consensus Algorithms for Airspace Management Application to the UAS-S4 Ehécatl

Distributed Dual Closed-Loop Model Predictive Formation Control for Collision-Free Multi-AUV System Subject to Compound Disturbances

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