Research on Collision Avoidance Algorithm of Unmanned Surface Vehicle Based on Deep Reinforcement Learning

Xia, Jiawei; Zhu, Xiaoxia; Liu, Zhikun; Luo, Yasong; Wu, Zhaodong; Wu, Qiuhan

doi:10.1109/jsen.2022.3222575

Cited by 16 publications

(4 citation statements)

References 30 publications

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“…AI-based methods, such as reinforcement learning (RL) [112][113][114] and self-organizing maps (SOMs) [115][116][117], have demonstrated remarkable strengths that elevate their effectiveness. On the one hand, RL algorithms can learn optimal paths by iteratively interacting with an environment, receiving feedback in the form of rewards, see Figure 10.…”

Section: Ai-based Methodsmentioning

confidence: 99%

Unlocking the Ocean 6G: A Review of Path-Planning Techniques for Maritime Data Harvesting Assisted by Autonomous Marine Vehicles

Zhao,

Bai

2024

JMSE

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Seamless integration of both terrestrial and non-terrestrial networks is crucial to providing full-dimensional wireless and ubiquitous coverage, particularly catering to those engaged in marine activities. Compared to terrestrial networks, wireless communications in the marine domain are still not satisfactory for ubiquitous connectivity. Featuring agile maneuverability and strong adaptive capability, autonomous marine vehicles (AMVs) play a pivotal role in enhancing communication coverage by relaying or collecting data. However, path planning for maritime data harvesting is one of the most critical issues to enhance transmission efficiency while ensuring safe sailing for AMVs; yet it has rarely been discussed under this context. This paper provides a comprehensive and holistic overview of path-planning techniques custom-tailored for the purpose of maritime data collection. Specifically, we commence with a general portrayal of fundamental models, including system architectures, problem formulations, objective functions, and associated constraints. Subsequently, we summarize the various algorithms, methodologies, platforms, tools, coding environments, and their practical implementations for addressing these models. Furthermore, we delve into the burgeoning applications of path planning in the realm of maritime data harvesting and illuminate potential avenues for upcoming research endeavors. We believe that future research may focus on developing techniques to adapt more intricate and uncertain scenarios, such as sensor failures, inaccurate state estimations, complete modeling of communication channels, ocean dynamics, and application of heterogeneous systems.

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Section: Ai-based Methodsmentioning

confidence: 99%

Unlocking the Ocean 6G: A Review of Path-Planning Techniques for Maritime Data Harvesting Assisted by Autonomous Marine Vehicles

Zhao,

Bai

2024

JMSE

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“…(5) intelligent optimisation algorithms, for instance, neural networks and reinforcement learning. 19 The first three methods generate formation strategies that accommodate the internal structure of a formation and its relationship with obstacle avoidance. However, these strategies are not suitable for USVs because of their underactuated nature and lack of agile response.…”

Section: State Of the Art Approachesmentioning

confidence: 99%

“…(5) intelligent optimisation algorithms, for instance, neural networks and reinforcement learning. 19…”

Section: Introductionmentioning

confidence: 99%

Underactuated unmanned surface vehicles formation of obstacle avoidance and assembly: A disturbed fluid-based solution

Liu,

Zhang,

Sui

et al. 2024

Measurement and Control

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Unmanned surface vehicles (USVs) are highly manoeuvrable and autonomous, and hold significant potential for both military and civilian applications, particularly in formation operations. However, because of their underactuated nature, USVs struggle to navigate in complex maritime conditions during formation. At present, most of the technology is devoted to Unmanned Areial Vehicles and ground robots; these methods cannot be well applied to underactuated USVs. Moreover, the rationality of local path planning decision-making for underactuated USVs formation is still lacking. This study proposes an interfered fluid dynamic system (IFDS)-based local path planning method, called USV-IFDS, specifically designed for the formation of underactuated USVs. This method incorporates the IFDS obstacle avoidance approach, while adapting it through modifications and the inclusion of the kinematic constraints of USVs, thereby enhancing its applicability to the maritime environment. By decomposing the flow field velocity vector and implementing a formation control strategy, we effectively address the challenges in forming underactuated USVs and enhance the efficiency of USV formation local path planning. The proposed formation technique is predicated on the highly robust virtual structure method. Simulations of formation local path planning indicate that our method produces smooth paths, therefore validating its practical applicability to underactuated USV formations.

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“…Trading strategy plays a very important role in financial asset trading. How to automatically select trading strategy in complex and dynamic financial markets is an important research direction of modern finance [1]. Market traders often buy and sell volatile assets with the aim of maximizing their total returns.…”

Section: Introductionmentioning

confidence: 99%

Optimal trading strategies based on time series analysis

Wang,

Zhao,

Zhang

et al. 2023

AEMR

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Quantitative investment has been widely used in the field of foreign finance, especially the rapid development of international investment in the past decade. And financial activity is an important field of national economic activity. The frequency of financial transactions is an important indicator of the complexity of a country's economy, so it is of great significance to study the optimal investment strategy. This article uses daily price streams from past investments in gold, cash, and bitcoin to determine whether traders should buy, hold, or sell assets in their portfolios. The outlier data were processed by boxplot analysis, and the EM algorithm based on maximum likelihood estimation was used to visualize the case data. The ARIMA model and GARCH model are used to establish the portfolio optimization model and obtain the best portfolio scheme. The time series prediction model is used to conduct specific quantitative analysis on gold and Bitcoin and obtain the investment forecast of the initial $1000 in the future.

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Research on Collision Avoidance Algorithm of Unmanned Surface Vehicle Based on Deep Reinforcement Learning

Cited by 16 publications

References 30 publications

Unlocking the Ocean 6G: A Review of Path-Planning Techniques for Maritime Data Harvesting Assisted by Autonomous Marine Vehicles

Unlocking the Ocean 6G: A Review of Path-Planning Techniques for Maritime Data Harvesting Assisted by Autonomous Marine Vehicles

Underactuated unmanned surface vehicles formation of obstacle avoidance and assembly: A disturbed fluid-based solution

Optimal trading strategies based on time series analysis

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