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Abstract <p>To address the global path planning challenge for Unmanned Surface Vehicles in complex maritime environments characterized by dense islands and narrow waterways, this paper proposes a Hybrid Multi-Strategy Adaptive RRT* algorithm. The method combines a dynamic region-based sampling strategy with an improved artificial potential field based dynamic extension strategy, which introduces random-node attraction, dynamic repulsion adjustments, and additional repulsive forces. Additionally, a hierarchical side-retreat escape mechanism is applied to enhance obstacle avoidance and search efficiency in complex environments. The algorithm also incorporates heading-angle constraints and adaptive step-size adjustment to ensure the path complies with USV kinematic properties. Furthermore, an improved NSGA-II algorithm is proposed to perform multi-objective optimization of path length, smoothness, and safety, and B-spline interpolation is used to generate continuous and executable paths. Simulation results show that, compared with the standard RRT* algorithm, the proposed HMA-RRT* algorithm achieves average reductions of 7.85% in path length, 66.96% in node count, 48.73% in computation time, and 25.7% in mean turning angle across four representative complex maritime environments. These improvements significantly enhance search efficiency, path smoothness, and planning feasibility, thereby providing a reliable and efficient path-planning solution for autonomous USV navigation in complex maritime conditions.</p> Graphical abstract <p></p>

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HMA-RRT*: A hybrid multi-strategy adaptive RRT* algorithm for USV path planning in complex maritime environments

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月9日

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  • Zhenpeng Jiang,
  • Qingquan Liu,
  • Ende Wang,
  • Yu Wang,
  • Jiyun Wang

摘要

Abstract

To address the global path planning challenge for Unmanned Surface Vehicles in complex maritime environments characterized by dense islands and narrow waterways, this paper proposes a Hybrid Multi-Strategy Adaptive RRT* algorithm. The method combines a dynamic region-based sampling strategy with an improved artificial potential field based dynamic extension strategy, which introduces random-node attraction, dynamic repulsion adjustments, and additional repulsive forces. Additionally, a hierarchical side-retreat escape mechanism is applied to enhance obstacle avoidance and search efficiency in complex environments. The algorithm also incorporates heading-angle constraints and adaptive step-size adjustment to ensure the path complies with USV kinematic properties. Furthermore, an improved NSGA-II algorithm is proposed to perform multi-objective optimization of path length, smoothness, and safety, and B-spline interpolation is used to generate continuous and executable paths. Simulation results show that, compared with the standard RRT* algorithm, the proposed HMA-RRT* algorithm achieves average reductions of 7.85% in path length, 66.96% in node count, 48.73% in computation time, and 25.7% in mean turning angle across four representative complex maritime environments. These improvements significantly enhance search efficiency, path smoothness, and planning feasibility, thereby providing a reliable and efficient path-planning solution for autonomous USV navigation in complex maritime conditions.

Graphical abstract