[1]ZHANG Honghan,WANG Yabo,LI Juan,et al.Dynamic path-planning method of UUV in an offshore complex environment[J].CAAI Transactions on Intelligent Systems,2024,19(1):114-121.[doi:10.11992/tis.202302028]
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Dynamic path-planning method of UUV in an offshore complex environment

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 19 Number of periods: 2024 1 Page number: 114-121 Column: 学术论文—机器人 Public date: 2024-01-05
Title:
Dynamic path-planning method of UUV in an offshore complex environment
Author(s):
ZHANG Honghan;  WANG Yabo;  LI Juan;  WANG Yuanhui;  YAN Zheping
College of Intelligent Systems Science And Engineering, Harbin Engineering University, Harbin 150001, China
Keywords:
unmanned underwater vehicle; dynamic path-planning; rapidly exploring random tree; dynamic window approach; adaptive; underwater environment; local path-planning; obstacle avoidance
CLC:
TP273
DOI:
10.11992/tis.202302028
Abstract:
This paper proposes an algorithm that combines global and local dynamic path-planning methods to solve the dynamic path-planning problem for unmanned underwater vehicles (UUVs) in near-shore environments. First, a fast-expanding random tree algorithm based on adaptive target guidance is proposed to increase the directionality of random tree growth. The algorithm then achieves rapid convergence using turning and reselection strategies to reduce ineffective expansion. Further, an adaptive sub-node selection strategy is used after obtaining the global path to acquire sub-target points for the dynamic window approach. This approach decomposes the complex global dynamic task planning into multiple simple dynamic path-planning tasks, preventing the dynamic window approach from falling into local minima. Finally, the effectiveness and practicality of the algorithm are verified using simulation experiments of UUV departure tasks.
References:
[1] 严浙平, 王璐. UUV水下定位方法的研究现状与进展[J]. 哈尔滨工程大学学报, 2017, 38(7): 989–1000
YAN Zheping, WANG Lu. Research status and progress of UUV underwater localization[J]. Journal of Harbin Engineering University, 2017, 38(7): 989–1000
[2] LIANG Qingwei, LUO Mingyang, WANG Yida, et al. Multi-attacks effectiveness evaluation of UUV based on wake guidance[J]. Ocean engineering, 2022, 266: 112654.
[3] YAN Zheping, XU Da, CHEN Tao, et al. Modeling, strategy and control of UUV for autonomous underwater docking recovery to moving platform[C]//2017 36th Chinese Control Conference. Dalian: IEEE, 2017: 4807?4812.
[4] Lesire, Charles. “An Iterative A* Algorithm for Planning of Airport Ground Movements. ” ECAI. Vol. 2010. 2010
[5] SONG Zhihua, ZHANG Han, ZHANG Xiaojie, et al. Unmanned aerial vehicle coverage path planning algorithm based on cellular automata[C]//2019 15th International Conference on Computational Intelligence and Security. Macao: IEEE, 2020: 123?126.
[6] GUO Hong, HAN Dandan, ZHANG Hongguo. Using PSO to improve ant colony optimization algorithm[C]//International Conference on Software Intelligence Technologies and Applications & International Conference on Frontiers of Internet of Things 2014. Hsinchu: Institution of Engineering and Technology, 2014: 272?276.
[7] AL-KHAYYAT A T A, IBRAHIM A. Robot pathplanning by image segmentation using the fuzzy C-means algoritm and KNN algorithm[C]//2020 4th International Symposium on Multidisciplinary Studies and Innovative Technologies. Istanbul: IEEE, 2020: 1?4.
[8] WANG M, CUI P, CHEN J. Sectional polynomial smoothing method for autonomous driving reference trajectory[J]. Modern electronics technique, 2021, 44(15): 154?158.
[9] 赖永林, 林茂松, 梁艳阳. 基于三次非均匀B样条曲线的机器人轨迹规划算法研究[J]. 科学技术与工程, 2013, 13(35): 10511–10517
LAI Yonglin, LIN Maosong, LIANG Yanyang. The research on robot trajectory planning algorithm based on cubic non-uniform B-spline curve[J]. Science technology and engineering, 2013, 13(35): 10511–10517
[10] LIU Chaowei, WANG Hongjian, GU Yingmin, et al. UUV path planning method based on QPSO[C]//Global Oceans 2020. Biloxi: IEEE, 2021: 1?5.
[11] 刘祥, 叶晓明, 王泉斌, 等. 无人水面艇局部路径规划算法研究综述[J]. 中国舰船研究, 2021, 16(S1): 1–10
LIU Xiang, YE Xiaoming, WANG Quanbin, et al. Review on the research of local path planning algorithms for unmanned surface vehicles[J]. Chinese journal of ship research, 2021, 16(S1): 1–10
[12] 薛双飞. 基于改进A*算法的近海船舶路径规划[D]. 武汉: 武汉理工大学, 2018.
XUE Shuangfei. Route planning of offshore ships based on improved A* algorithm[D]. Wuhan: Wuhan University of Technology, 2018.
[13] CHOWDHURY M I, SCHWARTZ D G. UUV on-board path replanning using PRM-A[C]//Global Oceans 2020. Biloxi: IEEE, 2021: 1?8.
[14] 赵旭, 杨进, 陈琛, 等. 未知环境中水下无人航行器的路径规划方法[J]. 舰船科学技术, 2022, 44(2): 117–120
ZHAO Xu, YANG Jin, CHEN Chen, et al. Path planning method for unmanned undersea vehicle in unknown area[J]. Ship science and technology, 2022, 44(2): 117–120
[15] HE Juyi, WANG Hongjian, LIU Chaowei, et al. UUV path planning for collision avoidance based on ant colony algorithm[C]//2020 39th Chinese Control Conference. Shenyang: IEEE, 2020: 5528?5533.
[16] 李杨, 杜度, 于双宁, 等. 一种用于水下无人航行器的局部避碰路径规划方法研究[J]. 数字海洋与水下攻防, 2021, 4(6): 520–524
LI Yang, DU Du, YU Shuangning, et al. Research on the method of local collision avoidance path planning for unmanned underwater vehicle[J]. Digital ocean & underwater warfare, 2021, 4(6): 520–524
[17] LAVALLE S M, KUFFNER J J J. Randomized kinodynamic planning[J]. The international journal of robotics research, 2001, 20(5): 378–400.
[18] ZHOU Qiang, LIU Guangcai. UAV path planning based on the combination of A-star algorithm and RRT-star algorithm[C]//2022 IEEE International Conference on Unmanned Systems. Guangzhou: IEEE, 2022: 146?151.
[19] QURESHI A H, AYAZ Y. Intelligent bidirectional rapidly-exploring random trees for optimal motion planning in complex cluttered environments[J]. Robotics and autonomous systems, 2015, 68: 1–11.
[20] YAN Zheping, HAO Bing, LIU Yibo, et al. DD-RRT path planning and guidance in heading-vector field for a UUV recovery[C]//OCEANS 2016 MTS/IEEE Monterey. Monterey: IEEE, 2016: 1?5.
[21] 刘成菊, 韩俊强, 安康. 基于改进RRT算法的RoboCup机器人动态路径规划[J]. 机器人, 2017, 39(1): 8–15
LIU Chengju, HAN Junqiang, AN Kang. Dynamic path planning based on an improved RRT algorithm for RoboCup robot[J]. Robot, 2017, 39(1): 8–15
[22] 尹高扬, 周绍磊, 吴青坡. 基于改进RRT算法的无人机航迹规划[J]. 电子学报, 2017, 45(7): 1764–1769
YIN Gaoyang, ZHOU Shaolei, WU Qingpo. An improved RRT algorithm for UAV path planning[J]. Acta electronica sinica, 2017, 45(7): 1764–1769
[23] 王硕, 段蓉凯, 廖与禾. 机器人路径规划中快速扩展随机树算法的改进研究[J]. 西安交通大学学报, 2022, 56(7): 1–8
WANG Shuo, DUAN Rongkai, LIAO Yuhe. Research on improvement of rapidly exploring random tree algorithm in robot path planning[J]. Journal of Xi’an Jiaotong University, 2022, 56(7): 1–8
[24] 张瑞, 周丽, 刘正洋. 融合RRT*与DWA算法的移动机器人动态路径规划[J/OL]. 系统仿真学报: 1?12. [2023?04?15]. http://10.16182/j.issn1004731x.joss.22-1543.
ZHAGN Rui, ZHOU Li，LIU Zhengyang. Dynamic path planning for mobile robot based on RRT* an dynamic window approach[J/OL]. Journal of system simulation: 1-12. (2023?03?24)[2023?04?15]. http://10.16182/j.issn1004731x.joss.22-1543.
[25] HAJI M N, TRAN J, NORHEIM J, et al. Design and testing of auv docking modules for a renewably powered offshore auv servicing platform[C]//International Conference on Offshore Mechanics and Arctic Engineering. New York: American Society of Mechanical Engineers, 2020, 84386: V06BT06A024.
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