Pursuit-evasion game strategy of USV based on deep reinforcement learning in complex multi-obstacle environment

Qu, Xiuqing; Gan, Wenhao; Song, Dalei; Zhou, Liqin

doi:10.1016/j.oceaneng.2023.114016

Cited by 30 publications

(5 citation statements)

References 16 publications

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“…Furthermore, there is a concerted effort to develop algorithms that seamlessly integrate with existing maritime systems and infrastructure to facilitate safe and efficient autonomous navigation in real-world environments. An emerging focus has been on enhancing precise navigation, which is accompanied by the refinement of algorithms to minimize navigation errors and the implementation of methods to track predetermined paths effectively [129]. Addressing the challenge of USV route planning amidst wave interference and navigating around unidentified obstacles remains a pertinent issue [130].…”

Section: From 2021 To Datementioning

confidence: 99%

Route Planning Algorithms for Unmanned Surface Vehicles (USVs): A Comprehensive Analysis

Hashali,

Yang,

Xiang

2024

JMSE

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This review paper provides a structured analysis of obstacle avoidance and route planning algorithms for unmanned surface vehicles (USVs) spanning both numerical simulations and real-world applications. Our investigation encompasses the development of USV route planning from the year 2000 to date, classifying it into two main categories: global and local route planning. We emphasize the necessity for future research to embrace a dual approach incorporating both simulation-based assessments and real-world field tests to comprehensively evaluate algorithmic performance across diverse scenarios. Such evaluation systems offer valuable insights into the reliability, endurance, and adaptability of these methodologies, ultimately guiding the development of algorithms tailored to specific applications and evolving demands. Furthermore, we identify the challenges to determining optimal collision avoidance methods and recognize the effectiveness of hybrid techniques in various contexts. Remarkably, artificial potential field, reinforcement learning, and fuzzy logic algorithms emerge as standout contenders for real-world applications as consistently evaluated in simulated environments. The innovation of this paper lies in its comprehensive analysis and critical evaluation of USV route planning algorithms validated in real-world scenarios. By examining algorithms across different time periods, the paper provides valuable insights into the evolution, trends, strengths, and weaknesses of USV route planning technologies. Readers will benefit from a deep understanding of the advancements made in USV route planning. This analysis serves as a road map for researchers and practitioners by furnishing insights to advance USV route planning and collision avoidance techniques.

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Section: From 2021 To Datementioning

confidence: 99%

Route Planning Algorithms for Unmanned Surface Vehicles (USVs): A Comprehensive Analysis

Hashali,

Yang,

Xiang

2024

JMSE

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“…[22] introduced a DRL algorithm tailored for collision avoidance among multiple USVs operating in complex scenarios, Ref. [23] proposed a novel collision avoidance method using DRL, and [24] developed evasion strategies for USVs in multi-obstacle environments.…”

Section: Reinforcement Learning Agent For Usv Navigation and Controlmentioning

confidence: 99%

Design and Implementation of a Low-Cost Intelligent Unmanned Surface Vehicle

Chaysri,

Spatharis,

Vlachos

et al. 2024

Sensors

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This article describes the design and construction journey of a self-developed unmanned surface vehicle (USV). In order to increase the accessibility and lower the barrier of entry we propose a low-cost (under EUR 1000) approach to the vessel construction with great adaptability and customizability. This design prioritizes minimal power consumption as a key objective. It focuses on elucidating the intricacies of both the design and assembly processes involved in creating an economical USV. Utilizing easily accessible components, the boat outlined in this study has been already participated in the 1st Aegean Ro-boat Race 2023 competition and is tailored for entry into similar robotic competitions. Its primary functionalities encompass autonomous sea navigation coupled with sophisticated collision avoidance capabilities. Finally, we studied reinforcement learning strategies for constructing a robust intelligent controller for the task of USV navigation under disturbances and we show some preliminary simulation results we have obtained.

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“…Qi et al [ 28 ] proposed a deep Q-network approach to guide the evader to escape from the pursuer based on a self-play mechanism. Qu et al [ 29 ] proposed a deep reinforcement learning approach to generate pursuit and evasion trajectories for unmanned surface vehicles. The proposed approach considered multi-obstacle influences in the water surface environment.…”

Section: Introductionmentioning

confidence: 99%

Bio-Inspired Neural Network for Real-Time Evasion of Multi-Robot Systems in Dynamic Environments

Li,

Yang

2024

Biomimetics

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In complex and dynamic environments, traditional pursuit–evasion studies may face challenges in offering effective solutions to sudden environmental changes. In this paper, a bio-inspired neural network (BINN) is proposed that approximates a pursuit–evasion game from a neurodynamic perspective instead of formulating the problem as a differential game. The BINN is topologically organized to represent the environment with only local connections. The dynamics of neural activity, characterized by the neurodynamic shunting model, enable the generation of real-time evasive trajectories with moving or sudden-change obstacles. Several simulation and experimental results indicate that the proposed approach is effective and efficient in complex and dynamic environments.

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Pursuit-evasion game strategy of USV based on deep reinforcement learning in complex multi-obstacle environment

Cited by 30 publications

References 16 publications

Route Planning Algorithms for Unmanned Surface Vehicles (USVs): A Comprehensive Analysis

Route Planning Algorithms for Unmanned Surface Vehicles (USVs): A Comprehensive Analysis

Design and Implementation of a Low-Cost Intelligent Unmanned Surface Vehicle

Bio-Inspired Neural Network for Real-Time Evasion of Multi-Robot Systems in Dynamic Environments

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