未知环境下融合局部-全局策略的AUV路径规划

doi:10.16182/j.issn1004731x.joss.24-0983

摘要/Abstract

摘要：

为解决现有的路径规划算法难以高效探索并生成高质量轨迹的问题，提出融合局部-全局策略的路径规划算法。通过滚动窗口技术，将全局一次性路径规划问题转化为多次局部规划的迭代过程。在全局探测阶段，使用滚动窗口来决定高层路径分支，确定分支航点，完善局部路径的计算。在局部探测阶段，提出一种改进RRT-Connect算法，通过将自适应圆采样与动态步长相结合，减少冗余采样点的产生。仿真结果表明：在未知复杂环境中，该路径规划算法能够高效实现AUV路径搜索，改进RRT-Connect算法具备更快的收敛速度，生成路径更短，更加贴近全局路径。

关键词: 路径规划, 水下未知空间, 滚动规划, 改进RRT-Connect算法

Abstract:

Existing path planning algorithms often struggle to efficiently explore and generate high-quality trajectories. To address this issue, this paper proposes a path planning algorithm that integrates local-global strategies. By employing the rolling window technique, the global one-time path planning problem is transformed into an iterative process of multiple local planning stages. During the global exploration phase, the rolling window is used to determine high-level path branches and to identify branch waypoints, thereby refining the calculation of local paths. In the local exploration phase, an improved RRT-Connect algorithm is proposed, which combines adaptive circular sampling with dynamic step length to reduce redundant sampling points. Simulation experiments demonstrate that the algorithm can efficiently achieve AUV path search in unknown complex environments. The improved RRT-Connect algorithm features faster convergence, generates shorter paths, and produces paths that are closer to the global path.

Key words: path planning, unknown underwater space, rolling planning, improved RRT-Connect algorithm

中图分类号:

TP242.6

孟文龙,濮彦博,龚亚 . 未知环境下融合局部-全局策略的AUV路径规划[J]. 系统仿真学报, 2026, 38(4): 889-902.

Meng Wenlong,Pu Yanbo,Gong Ya . AUV Path Planning Integrating Local-global Strategies in Unknown Environments[J]. Journal of System Simulation, 2026, 38(4): 889-902.

图/表 16

图1

图2

图3

图4

图5

表1

仿真参数

主要仿真参数	量值
排斥系数 $u$	1.5
排斥效应上限 $D s u p$	160
排斥效应下限 $D i n f$	5
候选子目标点数 $m$	10
滚动窗口半径	100
固定步长 $s$	30
最大迭代次数	1 000
每一场景测试次数	50
重连搜索半径 $r$	20

表1

图6

表2

图7

表3

图8

图9

图10

表4

图11

图12

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探测范围/m	地图I		地图III
探测范围/m	时间/ms	长度	时间/ms	长度
10	124	2 021	247	1 889
50	47	1 982	123	1 752
100	15	1 873	54	1 658
200	11	1 733	36	1 564
400	7	1 654	19	1 497
1 000	4	1 564	11	1 354

方法	评价指标	地图I	地图III
本文方法	平均时间/ms	1.74	2.91
本文方法	平均长度	1 582	1 481
RRT^[29]	平均时间/ms	45.12	87.96
RRT^[29]	平均长度	1 656	1 698
RRT-Connect^[22]	平均时间/ms	3.38	5.6
RRT-Connect^[22]	平均长度	1 609	1 507
RRT*^[30]	平均时间/ms	3 613.24	10 329.6
RRT*^[30]	平均长度	1 335	1 281