为解决RRT*算法在固定风场环境下难以有效处理无人帆船非凸、非对称路径成本优化的局限性，以最小化航行时间为目标，提出基于改进RRT*的无人帆船路径规划算法。首先，设计基于Beta分布的自适应采样策略，建立具有方向偏好性的非均匀采样机制，实现目标导向区域的偏置采样。接着，提出结合船速实时反馈动态调整扩展步长的策略，优化搜索效率。随后，构建多项式插值模型模拟帆船在不同风向下的速度，建立航速与风向角的定量关系以实现航行时间的精确计算。最后，使用二分法优化航行路径进一步缩短航行时间，并通过贝塞尔曲线实现路径平滑。通过Matlab R2024环境进行仿真，结果表明：在固定风场的不同场景中，基于改进RRT*算法规划的路径相较于RRT*和Q-RRT*算法，其航行时间显著缩短。改进后的RRT*算法能够为无人帆船在风场环境下自主航行提供可靠的路径规划支持。

To address the limitations of the RRT* algorithm in effectively optimizing non-convex and asymmetric path costs for unmanned sailboats in fixed wind fields, this study proposes an improved RRT*-based path planning algorithm with the objective of minimizing sailing time. Firstly, an adaptive sampling strategy based on the Beta distribution is designed to establish a directional-biased non-uniform sampling mechanism, enabling targeted sampling in goal-oriented regions. Subsequently, a dynamic step-size adjustment strategy incorporating real-time vessel speed feedback is introduced to enhance search efficiency. Next, a polynomial interpolation model is constructed to simulate sailboat speed under varying wind angles, establishing a quantitative relationship between sailing speed and wind angle to achieve precise sailing time calculation. Finally, the bisection method is employed to further optimize the sailing path for reduced voyage duration, with Bézier curves utilized for path smoothing. Simulations conducted in the Matlab R2024a environment demonstrate that, across diverse fixed-wind scenarios, the proposed improved RRT* algorithm significantly reduces sailing time compared to both standard RRT* and Q-RRT* algorithms. The enhanced algorithm provides reliable path planning support for autonomous navigation of unmanned sailboats in wind field environments.