针对水下仿生航行器在路径规划中所面临的全局最优性、实时响应及鲁棒性挑战，本研究创新性地提出了一种结合改进快速随机搜索算法的路径规划策略。该策略首先融合传统的快速随机探索树（RRT）算法与蒙特卡洛算法，旨在显著提升节点扩展的平滑度，减少路径规划的长度，从而克服传统方法路径质量差、曲折不光滑以及容易陷入局部最优的局限性。进一步地，设计基于视线（Line-Of-Sight，LOS）算法的航向修正模块，以增强航行器在复杂水下环境中的导航精度与稳定性。通过三维仿真环境的测试，验证了所提算法的有效性与可行性。
To address the challenges of global optimality, real-time response, and robustness in path planning for bio-inspired underwater vehicles, this study proposes an innovative path planning strategy based on an improved rapidly-exploring random tree (RRT) algorithm. This strategy first integrates the traditional fast stochastic tree (RRT) algorithm and the Monte Carlo algorithm, aiming to significantly improve the smoothness of node extension and reduce the length of path planning, so as to overcome the limitations of poor path quality, unsmooth twists and turns, and easy to fall into local optimum. Furthermore, a heading correction module based on the Line-of-Sight (LOS) algorithm is designed to improve navigation accuracy and stability in complex underwater environments. Extensive simulations in a 3D virtual environment demonstrate the effectiveness and feasibility of the proposed algorithm.
2026,48(6): 82-89 收稿日期：2025-7-31
DOI：10.3404/j.issn.1672-7649.2026.06.012
分类号：U674.91；TP242.6
基金项目：海南省重点研发项目（DSTIC-CYCJ-2022007）
作者简介：王艺为(2001-),女,博士研究生,研究方向为水下航行器路径规划
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