面向动态障碍环境的AUV三维路径规划

陈超洋; 唐于特; 黄毅; 刘智群

doi:10.11993/j.issn.2096-3920.2025-0008

面向动态障碍环境的AUV三维路径规划

doi: 10.11993/j.issn.2096-3920.2025-0008

陈超洋^{1, 2, 3,},
唐于特^{1, 2,},
黄毅^{1, 2,},
刘智群^2,

1.
湖南科技大学三亚研究院, 海南三亚, 572000
2.
湖南科技大学信息与电气工程学院, 湖南湘潭, 411100
3.
复杂系统智能控制与运维湖南省重点实验室, 湖南湘潭, 411100

基金项目: 国家自然科学基金优秀青年项目(62222306); 国家自然科学基金青年项目(62403193); 湖南省科技创新计划项目(2024RC9015); 湖南科技大学博士科研启动基金项目(20240190); 湖南省研究生科研创新项目(CX20240872).

详细信息

作者简介:
陈超洋(1984-), 男, 博士, 教授, 主要研究方向为智能探测与控制

中图分类号: TJ630; U674
计量
- 文章访问数: 76
- HTML全文浏览量: 22
- PDF下载量: 34
- 被引次数: 0
出版历程
- 收稿日期: 2025-01-13
- 修回日期: 2025-03-13
- 录用日期: 2025-03-24
- 网络出版日期: 2025-06-03

Three-Dimensional Path Planning of AUVs in Dynamic Obstacle Environments

CHEN Chaoyang^{1, 2, 3
,},
TANG Yute^{1, 2
,},
HUANG Yi^{1, 2
,},
LIU Zhiqun^2
,

1.
Sanya Institute of Hunan University of Science and Technology, Sanya 572024, China
2.
School of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan 411100, China
3.
Hunan Key Laboratory of Intelligent Control and aintenance for Complex Systems, Xiangtan 411100, China )

摘要

摘要: 传统算法在自主水下航行器(AUV)面临的高维空间和动态障碍物环境中存在计算负担大、精度不足问题。针对复杂三维环境下动态障碍物对AUV路径规划的挑战, 文中提出一种基于改进的双重深度Q网络(DDQN)的AUV三维路径规划方法。通过网络结构优化和高效奖励函数设计, 显著提升了AUV路径规划效率和精度。此外, 引入了动态障碍物运动轨迹建模, 采用辛格模型和卡尔曼滤波算法实现障碍物状态的精确预测, 增强了AUV的动态避障能力。同时, 采用Basis样条函数对路径进行平滑处理, 改善了AUV航行路径的连续性与稳定性。仿真与实验结果表明, 所提方法在复杂动态环境下能够有效避免碰撞, 实时规划出安全且高效的航行路径。相比传统方法, DDQN算法在路径长度、避障成功率及计算效率方面均展现出明显优势, 有效解决了动态障碍环境中AUV的三维路径规划问题。
- 自主水下航行器 /
- 深度强化学习 /
- 三维路径规划 /
- 动态障碍物
Abstract: Traditional algorithms suffer from heavy computational burden and insufficient accuracy in the high-dimensional space and dynamic obstacle environment faced by autonomous underwater vehicles(AUVs). To overcome the challenges posed by dynamic obstacles to AUV path planning in complex three-dimensional environments, this study proposed a three-dimensional path planning method for AUVs based on an enhanced double deep Q-network(DDQN). By optimizing the network architecture and designing an efficient reward function, the AUV path planning efficiency and accuracy were significantly improved. Moreover, dynamic obstacle trajectories were modeled, and the Singer model, combined with the Kalman filter algorithm, was used to precisely predict obstacle states, thereby enhancing the dynamic obstacle avoidance capabilities of AUVs. Additionally, Basis spline functions were utilized to smooth the paths, thereby improving the path continuity and stability of AUVs. Simulation and experimental results demonstrate that the proposed approach effectively avoids collisions in complex dynamic environments and achieves real-time planning of safe and efficient paths. Compared to traditional methods, the DDQN algorithm shows significant advantages in terms of path length, obstacle avoidance success rate, and computational efficiency, effectively addressing the challenges associated with three-dimensional path planning of AUVs in dynamic obstacle environments.
- autonomous undersea vehicle /
- deep reinforcement learning /
- three-dimensional path planning /
- dynamic obstacle

HTML全文

图 1 圆柱形AUV示意图

Figure 1. Schematic diagram of cylindrical AUV

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图 2 三维空间动态障碍物轨迹预测图

Figure 2. Trajectory prediction diagram of three-dimensional space dynamic obstacle

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图 3 XYZ方向位置预测偏差分布图

Figure 3. Prediction deviation distribution map of position in XYZ directions

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图 4 动态避障试验结果

Figure 4. Test results of dynamic obstacle avoidance

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图 5 AUV三维路径规划试验结果

Figure 5. Experimental results of 3D path planning for AUV

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表 1 避障试验参数

Table 1. Test parameters of obstacle avoidance

试验编号	障碍物类型	障碍物位置 /运动方向	AUV起始位置	AUV目标位置	AUV初始航向角/(°)
1	静态	(15, 15, 15)	(0, 0, 0)	(30, 30, 30)	45
2	动态	横向穿越	(0, 0, 0)	(30, 30, 30)	45
3	动态	同向运动	(0, 0, 0)	(30, 30, 30)	45
4	动态	相对运动	(0, 0, 0)	(30, 30, 30)	45

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表 2 DDQN网络超参数

Table 2. Network hyperparameters of DDQN

参数	符号	值
经验回放池容量	n_m	5000
折扣因子	$\lambda $	0.98
探索率	$\varepsilon $	1.0
最小探索率	${\varepsilon _{\min }}$	0.05
探索率衰减	$ {\varepsilon _{{\mathrm{decay}}}} $	0.99
步数惩罚因子	k₁	0.6
洋流放缩因子	k₂	0.4

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表 3 不同算法试验结果比较

Table 3. Comparison of experimental results for different algorithms

算法	最小路径/m	平均路径/m	航行时间/s
PRM	1945.82	2104.50	4195.67
RRT	2298.57	2421.77	4623.94
APF	1899.48	1959.98	3941.80
DDQN	1789.98	1859.98	3545.93

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