A decision-theoretic approach to acquire environmental information for improved subsea search performance

Yetkin, Harun; Lutz, Collin C.; Stilwell, Daniel J.

doi:10.1016/j.oceaneng.2020.107280

Cited by 6 publications

(2 citation statements)

References 21 publications

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“…For instance, an energy-efficient motion planning strategy was provided in [6] to balance the communication energy consumption and prolong the network lifetime. Yetkin et al [7] incorporated the environment information into the path planning of underwater vehicles, through which a decision-theoreticbased subsea search algorithm was designed. In [8], the endto-end data freshness constraint was conducted to determine the paths of underwater vehicles, whose aim was to retrieve the collected data to control center as soon as possible.…”

Section: Introductionmentioning

confidence: 99%

Communication-Efficient and Collision-Free Motion Planning of Underwater Vehicles via Integral Reinforcement Learning

Yan

Cao

Yang

et al. 2024

IEEE Trans. Neural Netw. Learning Syst.

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Motion planning of underwater vehicles is regarded as a promising technique to make up the flexibility deficiency of underwater sensor networks (USNs). Nonetheless, the unique characteristics of underwater channel and environment make it challenging to achieve the above mission. This article is concerned with a communication-efficient and collision-free motion planning issue for underwater vehicles in fading channel and obstacle environment. We first develop a model-based integral reinforcement learning (IRL) estimator to predict the stochastic signal-to-noise ratio (SNR). With the estimated SNR, an integrated optimization problem for the codesign of communication efficiency and motion planning is constructed, in which the underwater vehicle dynamics, communication capacity, collision avoidance, and position control are all considered. In order to tackle this problem, a model-free IRL algorithm is designed to drive underwater vehicles to the desired position points while maximizing the communication capacity and avoiding the collision. It is worth mentioning that, the proposed motion planning solution in this article considers a realistic underwater communication channel, as well as a realistic dynamic model for underwater vehicles. Finally, simulation and experimental results are demonstrated to verify the effectiveness of the proposed approach.

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Section: Introductionmentioning

confidence: 99%

Communication-Efficient and Collision-Free Motion Planning of Underwater Vehicles via Integral Reinforcement Learning

Yan

Cao

Yang

et al. 2024

IEEE Trans. Neural Netw. Learning Syst.

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“…One or more searching agent(s) may locate the target (or multiple targets) of interest with this dedicate path [1][2][3][4][5][6]. The research area was introduced by Koopman [7] and has been extensively investigated [8][9][10][11][12][13][14] in the operations research community. Specifically, the multi-unmanned aerial vehicle (UAV) collaborative search [15] is one of the most basic and challanging tasks in the area and has been applied in numerous practical scenarios such as search-and-deliverance missions [8] and disaster management [16].…”

Section: Introductionmentioning

confidence: 99%

A novel cooperative searching architecture for multi‐unmanned aerial vehicles under restricted communication

Ran

Sun

Song

et al. 2021

Asian Journal of Control

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Searching for a distinguishable lost target in a bounded area automatically with unmanned aerial vehicle (UAV) is a fundamental problem in the theory of physical search. This paper studies the problem in detail, presents a new and more realistic Bayesian formula representing the problem by taking communication capability of UAVs into consideration, and focuses on the case of applying a team of multiple cooperative UAVs in the field for the search, where each UAV can autonomously search the area for the target using Bayesian filtering algorithm. The perturbation of the searching performance by the different prior belief probability distribution functions is investigated. Empirical evidence findings illustrate that our approach yields improved accuracy.

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A new coverage path planning algorithm for unmanned surface mapping vehicle based on A-star based searching

Zhao

et al. 2022

Applied Ocean Research

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A decision-theoretic approach to acquire environmental information for improved subsea search performance

Cited by 6 publications

References 21 publications

Communication-Efficient and Collision-Free Motion Planning of Underwater Vehicles via Integral Reinforcement Learning

Communication-Efficient and Collision-Free Motion Planning of Underwater Vehicles via Integral Reinforcement Learning

A novel cooperative searching architecture for multi‐unmanned aerial vehicles under restricted communication

A new coverage path planning algorithm for unmanned surface mapping vehicle based on A-star based searching

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