DRL-based target interception strategy design for an underactuated USV without obstacle collision

Zhang, Chenming; Peng, Cheng; Lin, Bin; Zhang, Wensheng; Xie, Wei

doi:10.1016/j.oceaneng.2023.114443

Cited by 3 publications

(1 citation statement)

References 19 publications

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“…Name of Algorithm Main Features [137] APF-DQN algorithm • Able to handle COLREGS collision avoidance • APF algorithm was employed to improve the action space for DNQ algorithm as well as its reward function • DQN utilizes data acquired from virtual live sensors as its input information [138] AAFMS or the adaptive adjustable fast marching square • FMS-driven approach has been enhanced to align route planning with the stipulations of the COLREGS • A fitness assessment function has been introduced to appraise the suitability of planned routes while accounting for heterogeneity among USVs • An anisotropic element has been incorporated into the FMS-driven approach to adapt the potential field in accordance with the varying characteristics of USVs [139] Deep deterministic policy gradient (DDPG) algorithm Despite these weaknesses, algorithms validated in real-world scenarios represent significant progress in USV route planning. Their strengths lie in their practical applicability and effectiveness, which offers reliable navigation solutions for autonomous maritime vehicles.…”

Section: Referencementioning

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: Referencementioning

confidence: 99%