Energy-optimal Path Planning for AUV under Ocean Current Environment Based on Improved PSO-Lévy algorithm

YANG Huizhen; WANG Zijiang; ZHOU Zhuoyu; YANG Jun; LI Jianguo

doi:10.11993/j.issn.2096-3920.2023-0062

Article Contents

Article Navigation > Journal of Unmanned Undersea Systems > 2024 > Accepted Manuscript

YANG Huizhen, WANG Zijiang, ZHOU Zhuoyu, YANG Jun, LI Jianguo. Energy-optimal Path Planning for AUV under Ocean Current Environment Based on Improved PSO-Lévy algorithm[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2023-0062

Citation:

YANG Huizhen, WANG Zijiang, ZHOU Zhuoyu, YANG Jun, LI Jianguo. Energy-optimal Path Planning for AUV under Ocean Current Environment Based on Improved PSO-Lévy algorithm[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2023-0062

Citation:

PDF( 2276 KB)

Energy-optimal Path Planning for AUV under Ocean Current Environment Based on Improved PSO-Lévy algorithm

doi: 10.11993/j.issn.2096-3920.2023-0062

YANG Huizhen^{1, 2
,},
WANG Zijiang^{1, 2
,},
ZHOU Zhuoyu^{1, 2
,},
YANG Jun^{1, 2
,},
LI Jianguo^{1, 2
,}

1.
School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China
2.
Underwater Information and Control Laboratory, Xi’an 710072, China

Received Date: 2023-05-17
Accepted Date: 2023-09-18
Rev Recd Date: 2023-09-11

Available Online: 2024-01-31

Abstract

Abstract

To obtain energy-efficient obstacle avoidance paths of autonomous underwater vehicle (autonomous underwater vehicle, AUV) in dynamic current environment. A three-dimensional dynamic ocean current environment model based on dynamic current velocity field and underwater topographic obstacles was established. The objective function for optimal energy consumption based on kinematic constraint and the obstacle constraint is established. An improved particle-swarm-optimization-Lévy (PSO-Lévy) algorithm based on weight adjustment mechanism and random wandering mechanism is proposed. By executing long-step random walk operation and regulating the velocity of particles with certain probability, the improved algorithm can get longer step and jump out of local optimum. Simulation results show that the proposed method can plan the optimal path with low energy consumption and short distance, and can effectively ensure path safety and displaying higher efficiency.
- autonomous underwater vehicles,
- dynamic current,
- path planning,
- energy consumption

FullText(HTML)

References(24)

References

[1]	Zeng Z, Lian L, Sammut K, et al. A survey on path planning for persistent autonomy of autonomous underwater vehicles[J]. Ocean Engineering, 2015, 110(A): 303-313.
[2]	Xu H X, Jiang C L. Heterogeneous oceanographic exploration system based on USV and AUV: A survey of developments and challenges[J]. 中国科学院大学学报, 2021, 38(2): 145-159.
[3]	Kularatne D, Bhattacharya S, Hsieh M A. Going with the low: A graph based approach to optimal path planning in general flows[J]. Autonomous Robots, 2018, 42(7): 1369-1387. doi: 10.1007/s10514-018-9741-6
[4]	吴正平, 唐念, 陈永亮, 等. 基于改进人工势场法的AUV路径规划[J]. 化工自动化及仪表, 2014(12): 1421-1423. doi: 10.3969/j.issn.1000-3932.2014.12.021
[5]	Mao Z Y, Liu P L, Ding W J, et al. An improved genetic algorithm for optimal search path of unmanned underwater vehicles[C]//International Conference on Intelligent Robotics and Applications. Shenyang, China: Springer, Cham, 2019: 480-488.
[6]	Yang J C, Huo J M, Xi M. A time-saving path planning scheme for autonomous underwater vehicles with complex underwater conditions[J]. IEEE Internet of Things Journal, 2023, 10(2): 1001-1013. doi: 10.1109/JIOT.2022.3205685
[7]	Witt J, Dunbabin M. Go with the flow: Optimal AUV path planning in coastal environments[C]//Proceedings of the 2008 Australasian Conference on Robotics & Automation. Canberra: ARAA, 2009: 1-9.
[8]	刘晨霞, 朱大奇, 周蓓, 等. 海流环境下多AUV多目标生物启发任务分配与路径规划算法[J]. 控制理论与应用, 2022, 39(11): 2100-2107. doi: 10.7641/CTA.2022.11019
[9]	Lan W, Jin X, Wang T, et al. Improved RRT algorithms to solve path planning of multi-glider in time-varying ocean currents[J]. IEEE Access, 2021, 9: 158098-158115. doi: 10.1109/ACCESS.2021.3130367
[10]	Yang J C, Ni J F, Xi M, et al. Intelligent path planning of underwater robot based on reinforcement learning[J]. IEEE Transactions on Automation Science and Engineering, 2023, 20(3): 1983-1996. doi: 10.1109/TASE.2022.3190901
[11]	朱大奇, 孙兵, 李利. 基于生物启发模型的 AUV 三维自主路径规划与安全避障算法[J]. 控制与决策, 2015, 30(5): 9.
[12]	Chu Z Z, Wang F L, Lei T J, et al. Path planning based on deep reinforcement learning for autonomous underwater vehicles under ocean current disturbance[J]. IEEE Transactions on Intelligent Vehicles, 2023, 8(1): 108-120. doi: 10.1109/TIV.2022.3153352
[13]	Zhang W L, Liang S, Lu C, et al. SVF-RRT: A stream-based VF-RRT for USVs path planning considering ocean currents[J]. IEEE Robotics and Automation Letters, 2023, 8(4): 2413-2420. doi: 10.1109/LRA.2023.3245409
[14]	Valachovic H, Yang N, Sun J. Energy-optimal path planning of autonomous underwater vehicles using adaptive flow models[C]//Oceans 2022. Hampton Roads, USA: Department of Naval Architecture and Marine Engineering, 2022: 1-6.
[15]	Yan Z, Zhao Y, Zhang H. A method of UUV path planning with biased extension in ocean flows[C]//Proceedings of the 10th World Congress on Intelligent Control and Automation. Beijing, China: IEEE, 2012: 532-537.
[16]	Pan X, Wu X, Hou X. Research on global path planning for autonomous underwater vehicle considering ocean current[C]//2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference(IMCEC). Xi’an, China: IEEE, 2018: 790-793.
[17]	赵玉花, 石永康. 改进粒子群算法的多无人机航迹优化[J]. 电光与控制, 2023, 30(5): 29-33,51. doi: 10.3969/j.issn.1671-637X.2023.05.006
[18]	王学武, 严益鑫, 丁冬雁, 等. 基于Lévy-PSO算法的焊接机器人避障路径规划[J]. 上海交通大学学报, 2016, 50(10): 1517-1520,1525.
[19]	Zeng Z, Sammut K, Lian L, et al. A comparison of optimization techniques for AUV path planning in environments with ocean currents[J]. Robotics and Autonomous Systems, 2016, 82: 61-72. doi: 10.1016/j.robot.2016.03.011
[20]	黄书召, 田军委, 乔路, 等. 基于改进遗传算法的无人机路径规划[J]. 计算机应用, 2021, 41(2): 8. doi: 10.11772/j.issn.1001-9081.2020060797
[21]	Jones D, Hollinger G A. Planning energy-efficient trajectories in strong disturbances[J]. IEEE Robotics and Automation Letters, 2017, 2(4): 2080-2087. doi: 10.1109/LRA.2017.2719760
[22]	Øystein S, Petter N, Martin L. Trajectory planning for navigation aiding of autonomous underwater vehicles[J] IEEE Access, 2020, 8: 116586-116604.
[23]	Wang Q Y, Zhou D, Yin S, et al. Improved adaptive inertia and damping coefficient control strategy of VSG based on optimal damping ratio[C]//2022 International Power Electronics Conference (IPEC-Himeji 2022-ECCE Asia). Himeji, Japan: IEEE, 2022: 102-107.
[24]	刘鹏. 声呐区域探测效能评估技术研究[J]. 声学与电子工程, 2021(3): 8-11.