Multi-Strategy Fusion Path Planning Algorithm for Autonomous Surface Vessels with Dynamic Obstacles

Considering the complexity and variability inherent in maritime environments, path planning algorithms for navigation have consistently been a subject of intense research interest. Nonetheless, single-algorithm approaches often exhibit inherent limitations. Consequently, this study introduces a path...

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Published inJournal of marine science and engineering Vol. 13; no. 7; p. 1357
Main Authors Xie, Yongshun, Liu, Chengyong, He, Yixiong, Ma, Yong, Liu, Kang
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.07.2025
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Abstract Considering the complexity and variability inherent in maritime environments, path planning algorithms for navigation have consistently been a subject of intense research interest. Nonetheless, single-algorithm approaches often exhibit inherent limitations. Consequently, this study introduces a path planning algorithm for autonomous surface vessels (ASVs) that integrates an improved fast marching method (FMM) with the dynamic window approach (DWA) for underactuated ASVs. The enhanced FMM improves the overall optimality and safety of the determined path in comparison to the conventional approach. Concurrently, it effectively merges the local planning strengths of the DWA algorithm, addressing the safety re-planning needs of the global path when encountering dynamic obstacles, thus augmenting path tracking accuracy and navigational stability. The efficient hybrid algorithm yields notable improvements in the path planning success rate, obstacle avoidance efficacy, and path smoothness compared with the isolated employment of either FMM or DWA, demonstrating superiority and practical applicability in maritime scenarios. Through a comprehensive analysis of its control output, the proposed integrated algorithm accomplishes efficient obstacle avoidance via agile control of angular velocity while preserving navigational stability and achieves path optimization through consistent acceleration adjustments, thereby asserting its superiority and practical worth in challenging maritime environments.
AbstractList Considering the complexity and variability inherent in maritime environments, path planning algorithms for navigation have consistently been a subject of intense research interest. Nonetheless, single-algorithm approaches often exhibit inherent limitations. Consequently, this study introduces a path planning algorithm for autonomous surface vessels (ASVs) that integrates an improved fast marching method (FMM) with the dynamic window approach (DWA) for underactuated ASVs. The enhanced FMM improves the overall optimality and safety of the determined path in comparison to the conventional approach. Concurrently, it effectively merges the local planning strengths of the DWA algorithm, addressing the safety re-planning needs of the global path when encountering dynamic obstacles, thus augmenting path tracking accuracy and navigational stability. The efficient hybrid algorithm yields notable improvements in the path planning success rate, obstacle avoidance efficacy, and path smoothness compared with the isolated employment of either FMM or DWA, demonstrating superiority and practical applicability in maritime scenarios. Through a comprehensive analysis of its control output, the proposed integrated algorithm accomplishes efficient obstacle avoidance via agile control of angular velocity while preserving navigational stability and achieves path optimization through consistent acceleration adjustments, thereby asserting its superiority and practical worth in challenging maritime environments.
Audience Academic
Author Liu, Chengyong
Liu, Kang
Xie, Yongshun
He, Yixiong
Ma, Yong
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StartPage 1357
SubjectTerms Algorithms
Angular velocity
autonomous surface vessels
Efficiency
fusion algorithm
Local planning
Methods
Motion control
Navigation
Obstacle avoidance
Optimization
Path planning
Path tracking
Planning
Smoothness
Stability
Surface craft
underactuated kinematic model
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Title Multi-Strategy Fusion Path Planning Algorithm for Autonomous Surface Vessels with Dynamic Obstacles
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