The Collaborative Autonomy and Control Framework for Unmanned Surface Vehicle

Cui, Kuntao; Yang, Zuochang; Sun, Weiqiang

doi:10.1109/fcst.2015.53

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…Level 3, predictive consciousness, it is used to identify the motion parameters and future trajectories of possible threats and assesses the intentions of possible threats. Level 4, process optimization, it determines that when information from an MSA system does not meet the operation requirements, other means can be taken to obtain important information, such as requesting a mission plan from another platform or from a communication network [29].…”

Section: Marine Situational Awareness (Msa)mentioning

confidence: 99%

Learning-Based Task Offloading for Marine Fog-Cloud Computing Networks of USV Cluster

et al. 2019

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In recent years, unmanned surface vehicles (USVs) have made important advances in civil, maritime, and military applications. With the continuous improvement of autonomy, the increasing complexity of tasks, and the emergence of various types of advanced sensors, higher requirements are imposed on the computing performance of USV clusters, especially for latency sensitive tasks. However, during the execution of marine operations, due to the relative movement of the USV cluster nodes and the network topology of the cluster, the wireless channel states are changing rapidly, and the computing resources of cluster nodes may be available or unavailable at any time. It is difficult to accurately predict in advance. Therefore, we propose an optimized offloading mechanism based on the classic multi-armed bandit (MAB) theory. This mechanism enables USV cluster nodes to dynamically make offloading decisions by learning the potential computing performance of their neighboring team nodes to minimize average computation task offloading delay. It is an optimized algorithm named Adaptive Upper Confidence Boundary (AUCB) algorithm, and corresponding simulations are designed to evaluate the performance. The algorithm enables the USV cluster to effectively adapt to the marine vehicular fog computing networks, balancing the trade-off between exploration and exploitation (EE). The simulation results show that the proposed algorithm can quickly converge to the optimal computation task offloading combination strategy under heavy and light input data loads.

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Section: Marine Situational Awareness (Msa)mentioning

confidence: 99%

Learning-Based Task Offloading for Marine Fog-Cloud Computing Networks of USV Cluster

et al. 2019

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Joint Computation Offloading and Resource Management for USVs Cluster of Fog-Cloud Computing Architecture

Cui

Sun

2019

2019 IEEE International Conference on Smart Internet of Things (SmartIoT)

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Analysis of Design Directions for Unmanned Surface Vehicles (USVs)

Heo¹,

Kim²,

Kwon³

2017

JCC

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In recent years, because of the development of marine military science technology, there is a growing interest in the unmanned systems throughout the world. Also, the demand of Unmanned Surface Vehicles (USVs) which can be autonomously operated without the operator intervention is increasing dramatically. The growing interests lie in the facts that those USVs can be manufactured at much lower costs, and can be operated without the human fatigue, while can be sent to the hostile or quite dangerous areas that are inherently unhealthy for human operators. The utilization and the deployment of such vessels will continue to grow in the future. In this paper, along with the technological development of unmanned surface vehicles, we investigate and analyze the cases of already developed platforms and identify the trends of the technological advances. Additionally, we suggest the future directions of development.

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The Collaborative Autonomy and Control Framework for Unmanned Surface Vehicle

Cited by 3 publications

References 11 publications

Learning-Based Task Offloading for Marine Fog-Cloud Computing Networks of USV Cluster

Learning-Based Task Offloading for Marine Fog-Cloud Computing Networks of USV Cluster

Joint Computation Offloading and Resource Management for USVs Cluster of Fog-Cloud Computing Architecture

Analysis of Design Directions for Unmanned Surface Vehicles (USVs)

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