基于RRT*与改进人工势场的AUV路径规划方法

刘宇晴; 陈模江; 郝程鹏

doi:10.11993/j.issn.2096-3920.2026-0025

基于RRT*与改进人工势场的AUV路径规划方法

doi: 10.11993/j.issn.2096-3920.2026-0025

1.
中国科学院声学研究所, 北京, 100190
2.
中国科学院大学, 北京, 101408

详细信息

作者简介:
刘宇晴(1999-), 女, 硕士, 主要研究方向为自主水下航行器路径规划

中图分类号: TJ630; U674.941
计量
- 文章访问数: 4
- HTML全文浏览量: 2
- PDF下载量: 0
- 被引次数: 0
出版历程
- 收稿日期: 2026-01-21
- 修回日期: 2026-02-14
- 录用日期: 2026-03-02
- 网络出版日期: 2026-09-04

AUV Path Planning Method Based on RRT* and Improved Artificial Potential Field

1.
Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China
2.
University of Chinese Academy of Sciences, Beijing 101408, China

摘要

摘要: 针对复杂海流环境显著影响自主水下航行器(AUV)航行效能与能源效率的问题, 文中提出一种融合流场导向采样的双向快速扩展随机树与改进人工势场法的能耗优化路径规划算法(FAPF-Bi-RRT*)。首先, 设计基于流场信息的多策略采样概率修正方法, 通过计算流场与目标方向的一致性度量, 动态调整采样权重, 引导随机树向顺流及低能耗区域扩展, 解决传统 RRT* 盲目搜索的问题; 其次, 建立自适应流场势场模型, 将海流的推阻作用转化为势场梯度, 并在人工势场中引入流场增益自适应调节机制, 实时辅助节点扩展与重连, 避免逆流陷阱; 最后, 构建包含路径长度与能量消耗的复合代价函数进行渐进寻优。在真实地形与流场数据下的仿真结果表明, 该算法在保证概率完备性与避障能力的同时, 在强流场环境中与传统Bi-RRT*算法相比路径长度降低约14%, 路径能耗降低约10%, 实现了复杂海洋环境下的高效、节能路径规划。
- 路径规划 /
- AUV /
- RRT*算法 /
- 人工势场法 /
- 海流
Abstract: Aiming at the problem that the complex ocean current environment significantly affects the navigation efficiency of autonomous underwater vehicles (AUVs), this paper proposes an energy consumption-optimized path planning algorithm (FAPF-Bi-RRT*) that integrates bidirectional rapidly-exploring random tree with flow field-guided sampling and improved artificial potential field method. Firstly, a multi-strategy sampling probability correction method based on flow field information is designed. By calculating the consistency measure between the flow field and the target direction, the sampling weight is dynamically adjusted to guide the random tree to expand toward downstream and low-energy-consumption areas, thus solving the blind search problem of the traditional RRT*. Secondly, an adaptive flow field potential field model is established, which converts the pushing and resistance effects of ocean currents into potential field gradients. An adaptive adjustment mechanism of flow field gain is introduced into the artificial potential field to assist node expansion and reconnection in real time, thereby avoiding countercurrent traps. Finally, a composite cost function including path length and energy consumption is constructed for progressive optimization. Simulation results under real terrain and flow field data show that while ensuring probabilistic completeness and obstacle avoidance capability, the proposed algorithm reduces the path length by approximately 14% and the path energy consumption by approximately 10% compared with the traditional Bi-RRT* algorithm in strong flow field environments. This realizes efficient and energy-saving path planning in complex marine environments.
- path planning /
- autonomous underwater vehicle /
- RRT* /
- artificial potential field method /
- ocean currents

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图 1 FAPF-Bi-RRT*算法工作流程图

Figure 1. Flow chart of FAPF-Bi-RRT* method

下载: 全尺寸图片幻灯片

图 2 真实地形和流场图(112°E ~114°E, 16°N ~18°N)

Figure 2. Real terrain and flow fields

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图 3 无障碍物无流场路径规划

Figure 3. Path planning results without obstacles and flow fields

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图 4 有障碍物无流场路径规划

Figure 4. Path planning without flow field in obstacles environment

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图 5 真实地形流场路径规划

Figure 5. Path planning in real terrain flow fields

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图 6 真实地形流场中的FAPF-Bi-RRT*路径规划

Figure 6. FAPF-Bi-RRT* path planning in real terrain flow fields

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算法: FAPF-Bi-RRT*

1: T_a.Init(q_start); T_b.Init(q_goal);
2: for iter = 1 to N do
3: 　if isFlowFieldKnown != TRUE then
4: 　　FlowFieldData ← FlowFieldPerception(Env)
5: 　else
6: 　　FlowFieldData ← GetPredefinedFlowField()
7: 　end if
8: 　P_sampling ← CalSamplingProb(FlowFieldData)
9: 　q_rand ← GetRandSample(P_sampling)
10: 　q_nearest ← FindNearestNode(T_a, q_rand)
11: 　q_new ← Steer(q_nearest, q_rand)
12: 　q_new_opt ← APF_Optimize(q_new, FlowFieldData)
13: 　if CollisionFree(q_nearest, q_new_opt, Env) then
14: 　　T_a.AddNode(q_new_opt)
15: 　　T_a.Rewire()
16: 　　if TryConnectTrees(T_a, T_b) == TRUE then
17: 　　　Path ← ExtractPath(T_a, T_b)
18: 　　　return Path
19: 　　end if
20: 　　Swap(T_a, T_b)
21: 　end if
22: end for
23: return Failure

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表 2 主要超参数

Table 2. Main hyperparameters

符号	参数	数值
$ N $	最大迭代次数	5000
$ \lambda $	扩展步长	7
$ {P}_{\text{target}} $	目标采样概率	0.1
$ {d}_{\text{connect}} $	双树连接阈值	5
$ {k}_{\text{att}} $	引力增益系数	1
$ {k}_{\text{rep}} $	斥力增益系数	100
$ {d}_{\text{safe}} $	障碍物影响距离	20
$ {\lambda }_{1}, {\lambda }_{2} $	代价函数权重	0.5, 0.5
$ A $	水动力阻力系数	1
$ B $	转向能耗系数	0.5
$ {k}_{\text{flow}} $	流场势强度系数	1
$ \beta $	逆流调节因子	1
$ {\alpha }_{\max },{\alpha }_{\min } $	方向权重上下限	0.8, 0.2

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表 3 无障碍物无流场条件路径规划结果对比表

Table 3. Comparison of path planning results without obstacles and flow fields

算法	路径长度/m	计算时间/s	成功率
RRT*	329.92	0.353 0	0.98
Bi-RRT*	372.72	0.002 1	1.00
FAPF-Bi-RRT*	246.22	0.003 5	1.00

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表 4 有障碍物无流场条件路径规划结果对比表

Table 4. Comparison of path planning results without flow field in obstacles environment

算法	路径长度/m	计算时间/s	成功率
RRT*	329.41	0.341 7	0.98
Bi-RRT*	336.29	0.002 9	1.00
FAPF-Bi-RRT*	255.40	0.009 8	1.00

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表 5 真实地形流场路径规划结果对比表

Table 5. Comparison of path planning results in real terrain flow fields

算法	路径能耗(相对得分)	路径长度/km	总代价 ($ {\lambda }_{1}={\lambda }_{2}=0.5 $)(无量纲)
Bi-RRT*	146.46	145.34	145.90
FAPF-Bi-RRT*	130.29	124.50	127.40

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