自主水下航行器多终点航路规划的距离正则化混合水平集算法研究

doi:10.3969/j.issn.1000-1093.2020.04.014

兵工学报 ›› 2020, Vol. 41 ›› Issue (4): 750-762.doi: 10.3969/j.issn.1000-1093.2020.04.014

自主水下航行器多终点航路规划的距离正则化混合水平集算法研究

盛亮^1，2，邱志明³，于邵祯³，焦俊杰⁴

（1.海军航空大学航空基础学院，山东烟台 264001；2.海军工程大学兵器工程学院，湖北武汉 430033；3.海军研究院，北京 102442；4.南京理工大学机械工程学院，江苏南京 210094）

收稿日期:2019-06-12 修回日期:2019-06-12 上线日期:2020-06-02
作者简介:盛亮（1982—），男，讲师。E-mail:sl_hust@sina.com
基金资助:
国家自然科学基金项目（11504173）

A Novel Distance Regularized Hybrid Level Set Method for AUV Multi-destination Route Planning

SHENG Liang^1，2， QIU Zhiming³， YU Shaozhen³， JIAO Junjie⁴

(1.College of Aeronautical Basics, Naval Aeronautical University, Yantai 264001, Shandong, China；2.School of Weaponry Engineering, Naval University of Engineering, Wuhan 430033, Hubei, China; 3.Naval Research Academy, Beijing 102442, China; 4.School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu, China)

Received:2019-06-12 Revised:2019-06-12 Online:2020-06-02

摘要/Abstract

摘要： 为进一步提升水平集算法求解自主水下航行器(AUV)时间最优航路的计算效率，结合局部化思想和多项式距离正则化方程，提出一种用于AUV多终点航路规划的混合水平集算法。通过引入简单多项式距离正则化项，融合海流模型，推导新的水平集演化方程，并给出数值实现方法。所提算法无需重复初始化窄带且一次演化就能获得至多终点的所有最优航路集，解决了AUV多终点航路规划时计算效率不高、规划时间过长的问题。仿真结果表明，相较于蚁群算法和量子粒子群算法，在AUV的多终点航路规划中，混合水平集算法计算效率是蚁群算法的6.4倍，是量子粒子群算法的1.6倍，且鲁棒性更佳。

关键词: 自主水下航行器, 多项式距离正则化, 混合水平集, 多终点, 航路规划

Abstract: To further enhance the computational efficiency of level set method for solving AUV time-optimal route, a hybrid level set method for AUV multi-destination route planning is proposed by combining the localization and the polynomial distance regularized equation. The new level set evolution equation is derived and the numerical implementation method is given by introducing a simple polynomial distance regularized term and fusing the ocean current model. The proposed method can be used to obtain all the optimal routes to all destinations in one evolution without re-initialization of narrowband, and solve the problems of low computational efficiency and long-time multi-destination route planning of AUV. The simulated results show that the computational efficiency of the proposed method is 6.4 times of that of the ant colony algorithm, and 1.6 times of that of the quantum particle swarm algorithm in multi-destinations route planning of AUV, and it also has better robustness. Key

Key words: autonomousunderwatervehicle, polynomial-distanceregularization, hybridlevelset, multi-destination, routeplanning

中图分类号:

TP242.4

盛亮，邱志明，于邵祯，焦俊杰. 自主水下航行器多终点航路规划的距离正则化混合水平集算法研究[J]. 兵工学报, 2020, 41(4): 750-762.

SHENG Liang， QIU Zhiming， YU Shaozhen， JIAO Junjie. A Novel Distance Regularized Hybrid Level Set Method for AUV Multi-destination Route Planning[J]. Acta Armamentarii, 2020, 41(4): 750-762.

参考文献

［1］ YIGIT K. Path planning methods for autonomous underwater vehicles[D]. Cambridge, MA, US: Massachusetts Institute of Technology, 2011.
[2］ LOLLA T, UECKERMANN M P, YIAGˇU2IT K, et al. Path planning in time dependent flow fields using level set methods[C]∥Proceedings of 2012 IEEE International Conference on Robotics and Automation. Saint Paul, MN, US: IEEE, 2012:166-173.
[3］ LOLLA T, HALEY P J, LERMUSIAUX P F J. Time-optimal path planning in dynamic flows using level set equations:realistic applications[J]. Ocean Dynamics, 2014, 64(10):1399-1417.
[4］ SUBRAMANI D N, LERMUSIAUX P F J, HALEY P J, et al. Time-optimal path planning:real-time sea exercises[C]∥Proceedings of OCEANS 2017. Aberdeen, UK:IEEE, 2017.
[5］ SUBRAMANI D N, LOLLA T, JR P J H, et al. A stochastic optimization method for energy-based path planning [M]∥Dynamic Data-Driven Environmental Systems Science. Switzerland:Springer, 2015:347-358.
[6］ SUBRAMANI D N, LERMUSIAUX P F J. Energy-optimal path planning by stochastic dynamically orthogonal level-set optimization[J]. Ocean Modelling, 2016,100:57-77.
[7］刘厂, 孙天龙, 张志强, 等. 基于改进水平集的AUV时间最优航路规划方法[C]∥第36届中国控制会议. 大连:中国自动化学会控制理论专业委员会, 2017: 5722-5727.
LIU C, SUN T L, ZHANG Z Q, et al. Path planning of AUV based on modified level set method [C]∥Proceedings of the 36th Chinese Control Conference. Dalian:Professional Committee of Control Theory of China Automation Society, 2017:5722-5727. (in Chinese)
[8］ LI C M, XU C Y, GUI C F, et al. Distance regularized level set evolution and its application to image segmentation[J]. IEEE Transactions on Image Processing, 2010, 19(12):3243-3254.
[9］ WANG X, XU W J. Fusion kernel fuzzy C-means clustering and improved distance regularized level set evolution model of thyroid nodules segmentation[J]. Journal of Medical Imaging and Health Informatics, 2016, 6(5):1330-1336.

[10］ YUGANDER P, REDDY G R. Liver tumor segmentation in noisy CT images using distance regularized level set evolution based on fuzzy C-means clustering[C]∥Proceeding of the 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology. Bangalore, India:IEEE, 2017.
[11］叶锋, 李婉茹, 陈家祯, 等. 基于显著性区域检测和水平集的图像快速分割算法[J]. 电子与信息学报, 2017, 39(11):2661-2668.
YE F, LI W R, CHEN J Z, et al. Image fast segmentation algorithm based on saliency region detection and level set[J]. Journal of Electronics & Information Technology, 2017, 39(11):2661-2668. (in Chinese)
[12］张梦梦, 张泾周, 周三平, 等. 基于局部熵的边界与区域水平集图像分割模型[J]. 系统工程与电子技术, 2016, 38(12): 2884-2888.
ZHANG M M, ZHANG J Z, ZHOU S P, et al.Boundary and region level set method based on local entropy for image segmentation[J]. Systems Engineering and Electronics, 2016, 38(12):2884-2888. (in Chinese)
[13］张文辉, 郑福聪, 程文婕, 等. 基于双向演化的局部水平集组合算法用于拓扑优化[J]. 固体力学学报, 2018, 39(2):213-222.
ZHANG W H, ZHENG F C, CHENG W J, et al. Local-level-set-based algorithm combined with bi-directional evolutionary feature used for topology optimization[J]. Chinese Journal of Solid Mechanics, 2018, 39(2): 213-222. (in Chinese)
[14］ PENG D P, MERRIMAN B, OSHER S, et al. A PDE-based fast local level set method[J]. Journal of Computational Physics, 1999, 155(2):410-438.
[15］ ZHAO H K, CHAN T, MERRIMAN B, et al. A variational level set approach to multiphase motion[J]. Journal of Computational Physics, 1996, 127(1):179-195.
[16］ WU W F, WU Y, HUANG Q. An improved distance regularized level set evolution without re-initialization [C]∥Proceedings of IEEE 5th International Conference on Advanced Computational Intelligence. Nanjing, China:IEEE, 2012: 631-636.
[17］ ZENG Z, LAMMAS A, SAMMUT K, et al. Optimal path planning based on annular space decomposition for AUVs operating in a variable environment[C]∥Proceedings of IEEE OES Autonomous Underwater Vehicles. Southampton, UK:IEEE, 2012:1-9.
[18］冯炜, 张静远, 王众, 等. 海洋环境下基于量子行为粒子群优化的时间最短路径规划方法[J]. 海军工程大学学报, 2017, 29(6):72-77.
FENG W, ZHANG J Y, WANG Z, et al. A time-optimal path planning method based on quantum-behaved particle swam optimization in ocean environment[J]. Journal of Naval University of Engineering, 2017, 29(6):72-77. (in Chinese)
[19］ ZENG Z, LAMMAS A, SAMMUT K, et al. Shell space decomposition based path planning for AUVs operating in a variable environment[J]. Ocean Engineering, 2014, 91:181-195.
[20］ AGHABABA M P, AMROLLAHI M H, BORJKHANI M. Application of GA, PSO, and ACO algorithms to path planning of autonomous underwater vehicles[J]. Journal of Marine Science and Application, 2012, 11(3):378-386.
［21］夏卫星，杨晓东. 水下平台三维航路多约束指标规划的蚁群策略研究［J］. 兵工学报，2018，39（9）：1795-1803.
XIA W X, YANG X D. Research on ant colony strategy with multi-constraint and multi-response for three-dimensional route planning of underwater platform［J］. Acta Armamentarii, 2018,39(9):1795-1803. (in Chinese)

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自主水下航行器多终点航路规划的距离正则化混合水平集算法研究

A Novel Distance Regularized Hybrid Level Set Method for AUV Multi-destination Route Planning

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