[1]CHEN Shitong,LU Ziyu.3D path planning for low-speed underdriven AUV under ocean current disturbance[J].CAAI Transactions on Intelligent Systems,2025,20(2):425-434.[doi:10.11992/tis.202311004]
Copy

3D path planning for low-speed underdriven AUV under ocean current disturbance

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 20 Number of periods: 2025 2 Page number: 425-434 Column: 学术论文—智能系统 Public date: 2025-03-05
Title:
3D path planning for low-speed underdriven AUV under ocean current disturbance
Author(s):
CHEN Shitong;  LU Ziyu
College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China
Keywords:
automatic underwater vehicle; reinforcement learning; ocean current disturbance; path planning; 3D marine environment; deep Q-network; S57 charts; reward function
CLC:
TP242.6
DOI:
10.11992/tis.202311004
Abstract:
Ocean currents, which have a substantial impact on the navigation of low-speed, underdriven AUVs, can increase navigation time, raise energy consumption, and change the navigation trajectory. Therefore, planning an optimal navigation route that accounts for the disturbance of ocean currents is of considerable importance. This study mainly analyzes the mechanism by which ocean currents influence AUVs and proposes an improved DQN path planning algorithm based on the prioritized experience replay method. This modification addresses the problem of overestimation, which is a common issue when using a traditional DQN path planning algorithm. Additionally, the action design and reward functions are optimized. Path planning simulations are conducted in a 3D ocean environment, which is established based on S57 chart data and ocean current data provided by Earth & Space Research. Experimental results show that the improved DQN algorithm generates a more effective global path planning, offering a navigation route that minimizes time and energy consumption. This work provides a valuable reference for underwater AUV navigation, fully considering the impact of ocean current disturbances.
References:
[1] LI Xiaohong, YU Shuanghe. Three-dimensional path planning for AUVs in ocean currents environment based on an improved compression factor particle swarm optimization algorithm[J]. Ocean engineering, 2023, 280: 114610.
[2] 李娟, 张韵, 陈涛. 改进RRT算法在未知三维环境下AUV目标搜索中的应用[J]. 智能系统学报, 2022, 17(2): 368-375.
LI Juan, ZHANG Yun, CHEN Tao. Application of the improved RRT algorithm to AUV target search in an unknown 3D environment[J]. CAAI transactions on intelligent systems, 2022, 17(2): 368-375.
[3] BI Anyuan, ZHAO Fengye, ZHANG Xiantao, et al. Combined depth control strategy for low-speed and long-range autonomous underwater vehicles[J]. Journal of marine science and engineering, 2020, 8(3): 181.
[4] 刘甲, 周伟江, 马伟建. 低速AUV航渡过程中减少洋流影响的方法[J]. 舰船科学技术, 2020, 42(3): 88-92,97.
LIU Jia, ZHOU Weijiang, MA Weijian. Adaptive decision methods to reduce effect on ocean current to low-speed AUV sailing[J]. Ship science and technology, 2020, 42(3): 88-92,97.
[5] LIU Rundong, CHEN Zonggan, WANG Zijia, et al. Intelligent path planning for AUVs in dynamic environments: an EDA-based learning fixed height histogram approach[J]. IEEE access, 2019, 7: 185433-185446.
[6] 应泽光, 何琪. 基于改进A*算法的无人艇复杂水域路径规划[J]. 机电技术, 2022, 45(5): 33-35.
YING Zeguang, HE Qi. Complex water path planning for unmanned boats based on improved A* algorithm[J]. Mechanical & electrical technology, 2022, 45(5): 33-35.
[7] WANG Yanlong, LIANG Xu, LI Baoan, et al. Research and implementation of global path planning for unmanned surface vehicle based on electronic chart[C]//International Conference on Mechatronics and Intelligent Robotics. Kunming: Springer, 2017: 534-539.
[8] LI Ye, JIANG Yanqing, MA Shan, et al. Inverse speed analysis and low speed control of underwater vehicle[J]. Journal of central south university, 2014, 21(7): 2652-2659.
[9] DOHAN K. Ocean surface currents from satellite data[J]. Journal of geophysical research (oceans), 2017, 122(4): 2647-2651.
[10] HU Siyuan, XIAO Shuai, YANG Jiachen, et al. AUV path planning considering ocean current disturbance based on cloud desktop technology[J]. Sensors, 2023, 23(17): 7510.
[11] 李慧, 赵琳, 毛英. 海况干扰下潜艇六自由度运动分析[J]. 哈尔滨工程大学学报, 2017, 38(1): 94-100.
LI Hui, ZHAO Lin, MAO Ying. Analysis of six-degree-of-freedom motion in submarines under sea disturbance[J]. Journal of Harbin engineering university, 2017, 38(1): 94-100.
[12] XING Yuan, YOUNG R, NGUYEN G, et al. Optimal path planning for wireless power transfer robot using area division deep reinforcement learning[J]. Wireless power transfer, 2022, 9(1): 9921885.
[13] 郭兴海, 计明军, 张卫丹, 等. 可变洋流环境中自主水下航行器动态路径规划的改进QPSO算法[J]. 系统工程理论与实践, 2021, 41(8): 2112-2124.
GUO Xinghai, JI Mingjun, ZHANG Weidan, et al. Improved QPSO algorithm for dynamic path planning of autonomous underwater vehicles in variable ocean current environment[J]. Systems engineering-theory & practice, 2021, 41(8): 2112-2124.
[14] KIANI F, SEYYEDABBASI A, ALIYEV R, et al. Adapted-RRT: novel hybrid method to solve three-dimensional path planning problem using sampling and metaheuristic-based algorithms[J]. Neural computing and applications, 2021, 33(22): 15569-15599.
[15] 刘锋, 张严, 陈彦勇, 等. S-57电子海图的快速读取及可视化存储[J]. 舰船科学技术, 2014, 36(7): 108-112.
LIU Feng, ZHANG Yan, CHEN Yanyong, et al. Rapid reading and visual storage of the S-57 electronic chart[J]. Ship science and technology, 2014, 36(7): 108-112.
[16] 扈震, 杨之江, 马振强. 基于S-57标准的电子海图三维可视化[J]. 地球科学, 2010, 35(3): 471-474.
HU Zhen, YANG Zhijiang, MA Zhenqiang. Electronic navigation chart 3D visualization based on S-57[J]. Earth science, 2010, 35(3): 471-474.
[17] HU Hao, ZHOU Yongjian, WANG Tonghao, et al. A multi-task algorithm for autonomous underwater vehicles 3D path planning[C]//2020 3rd International Conference on Unmanned Systems. Harbin: IEEE, 2020: 972-977.
[18] KRIEG M, MOHSENI K. Dynamic modeling and control of biologically inspired vortex ring thrusters for underwater robot locomotion[J]. IEEE transactions on robotics, 2010, 26(3): 542-554.
[19] BIJLSMA S J. Optimal ship routing with ocean current included[J]. Journal of navigation, 2010, 63(3): 565-568.
[20] YANG Yang, LI Juntao, PENG Lingling. Multi-robot path planning based on a deep reinforcement learning DQN algorithm[J]. CAAI transactions on intelligence technology, 2020, 5(3): 177-183.
[21] HAN J. An efficient approach to 3D path planning[J]. Information sciences, 2019, 478: 318-330.
[22] LI Jianxin, CHEN Yiting, ZHAO Xiuniao, et al. An improved DQN path planning algorithm[J]. The journal of supercomputing, 2022, 78(1): 616-639.
[23] QIAO Lei, ZHANG Weidong. Trajectory tracking control of AUVs via adaptive fast nonsingular integral terminal sliding mode control[J]. IEEE transactions on industrial informatics, 2020, 16(2): 1248-1258.
[24] GU Yuwan, ZHU Zhitao, LYU Jidong, et al. DM-DQN: Dueling Munchausen deep Q network for robot path planning[J]. Complex & intelligent systems, 2023, 9(4): 4287-4300.
[25] 赵玉新, 杜登辉, 成小会, 等. 基于强化学习的海洋移动观测网络观测路径规划方法[J]. 智能系统学报, 2022, 17(1): 192-200.
ZHAO Yuxin, DU Denghui, CHENG Xiaohui, et al. Path planning for mobile ocean observation network based on reinforcement learning[J]. CAAI transactions on intelligent systems, 2022, 17(1): 192-200.
[26] WU Keyu, WANG Han, ESFAHANI M A, et al. Achieving real-time path planning in unknown environments through deep neural networks[J]. IEEE transactions on intelligent transportation systems, 2022, 23(3): 2093-2102.
[27] YANG Jian, XU Xin, YIN Dong, et al. A space mapping based 0–1 linear model for onboard conflict resolution of heterogeneous unmanned aerial vehicles[J]. IEEE transactions on vehicular technology, 2019, 68(8): 7455-7465.
[28] PHUNG M D, HA Q P. Safety-enhanced UAV path planning with spherical vector-based particle swarm optimization[J]. Applied soft computing, 2021, 107: 107376.
[29] ZHANG Jiaxin, LIU Meiqin, ZHANG Senlin, et al. Robust global route planning for an autonomous underwater vehicle in a stochastic environment[J]. Frontiers of information technology & electronic engineering, 2022, 23(11): 1658-1672.
[30] WANG Jiankun, JIA Xiao, ZHANG Tianyi, et al. Deep neural network enhanced sampling-based path planning in 3D space[J]. IEEE transactions on automation science and engineering, 2022, 19(4): 3434-3443.
[31] MELO A G, PINTO M F, MARCATO A L M, et al. Dynamic optimization and heuristics based online coverage path planning in 3D environment for UAVs[J]. Sensors, 2021, 21(4): 1108.
[32] TAN Li, ZHANG Hongtao, SHI Jiaqi, et al. A robust multiple unmanned aerial vehicles 3D path planning strategy via improved particle swarm optimization[J]. Computers and electrical engineering, 2023, 111: 108947.
[33] QI Yongqiang, LI Shuai, KE Yi. Three-dimensional path planning of constant thrust unmanned aerial vehicle based on artificial fluid method[J]. Discrete dynamics in nature and society, 2020: 4269193.
Similar References:

Memo

-

Last Update: 2025-03-05

Copyright © CAAI Transactions on Intelligent Systems