针对自主水下机器人在极地漂移海冰特征扫描中面临的路径规划滞后、避障困难及扫描覆盖率低等问题，提出一种融合高斯过程回归（Gaussian Process Regression，GPR）、快速探索随机树（Rapidly-exploring Random Tree Star，RRT*）和动态窗口法（Dynamic Window Approach，DWA）的扫描方法，实现漂移海冰底部区域的全覆盖探测。首先，利用GPR对海冰历史轨迹建模，并预测其短时位置变化。随后，将预测位置作为目标区域，结合人工势场法优化RRT*的节点扩展策略，生成全局扫描路径。在局部路径规划中，DWA以RRT*生成的全局路径为引导，结合邻近惩罚区域和一致性评价函数，实现动态避障并提高路径跟踪精度。仿真实验结果表明，所提融合算法在扫描覆盖率、避障性能和规划效率方面均优于传统方法，适用于漂移海冰底部的全覆盖扫描。
To address the issues of path planning delay, obstacle avoidance difficulty, and low scanning coverage faced by autonomous underwater vehicles in the characteristic scanning of drifting sea ice in polar regions, this paper proposes a scanning method that integrates Gaussian process regression (GPR), rapidly-exploring random Tree (RRT*), and dynamic window approach (DWA) to achieve full coverage detection of the underside of drifting sea ice. Firstly, GPR is used to model the historical trajectories of the sea ice and predict its short-term position changes. The predicted positions are then used as target regions, and the RRT* algorithm is employed to generate a global scanning path, with an artificial potential field method optimizing the node expansion strategy. In the local path planning stage, DWA uses the global path generated by RRT* as guidance, and incorporates a neighboring penalty region mechanism and a consistency evaluation function to implement dynamic obstacle avoidance while improving path tracking accuracy. Simulation results demonstrate that the proposed integrated algorithm outperforms traditional methods in terms of scanning coverage, obstacle avoidance performance, and planning efficiency, making it suitable for full-coverage scanning of the underside of drifting sea ice.
2026,48(6): 74-81 收稿日期：2025-11-28
DOI：10.3404/j.issn.1672-7649.2026.06.011
分类号：U66；TP242.2
基金项目：国家重点研发计划项目（2021YFC2801100）
作者简介：李俊贤(1999-),男,硕士研究生,研究方向为水下机器人路径规划
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