Research Progress of Real-Time Obstacle Avoidance Algorithms for Unmanned Undersea Vehicle: A Review
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摘要: 针对目前在研究自主水下航行器(AUV)实时避障算法过程中出现的重点难点及研究趋势, 文中从动态障碍物、多约束与多目标以及海流干扰3方面分析了水下实时避障算法的研究难点, 然后从人工势场法、模糊逻辑法和智能仿生算法3个方面重点阐述水下实时避障算法的研究进展。对比3种避障算法的研究现状得知, 通过修正势场函数、引入AUV运动约束、考虑障碍物相对速度和复杂海流影响等, 使改进的人工势场法克服了陷阱问题、局部极小值和目标不可达等问题, 成为解决AUV实时避障问题的重点研究方向。在躲避动态障碍物方面, 多种避障算法融合将成为一种趋势; 在多约束与多目标问题中, 能耗问题尤为重要却很少被作为参数引入到避障算法中, 具有很大的研究潜力; 针对海流干扰问题, 多数避障算法仅考虑了水平方向的定常流或涡流, 因此考虑三维海流干扰也是未来水下实时避障算法的研究方向之一。Abstract: Aiming at the difficulties and trends in the research of autonomous undersea vehicle(AUV) real-time obstacle avoidance algorithms, the difficulties in the research of underwater real-time obstacle avoidance algorithm are analyzed from three aspects of dynamic obstacles, multiple constraints and multiple objectives, and ocean current disturbance. Then, the research progress of underwater real-time obstacle avoidance algorithm is focused on the three aspects, i.e. artificial potential field method, fuzzy logic method, and intelligent bionic algorithm. By comparing the current researches on three kinds of obstacle avoidance algorithms, it is known that the improved artificial potential field method overcomes the problems of trap, local minimum, and goal unreachability, and becomes the key research direction to solve the real-time obstacle avoidance problem of unmanned undersea vehicle by modifying the potential field function, introducing the AUV motion constraint, considering the relative speed of obstacles and the influence of complex ocean current, etc. In the aspect of avoiding dynamic obstacles, the fusion of multiple obstacle avoidance algorithms will become a trend. As for the multi-constraint and multi-objective problem, energy consumption is particularly important but is rarely introduced into obstacle avoidance algorithm as a parameter, which has great research potential. For the ocean current disturbance, the majority of real-time obstacle avoidance algorithms only consider steady flow or eddy current in the horizontal direction, thus, consideration of the three-dimensional ocean current disturbance will also become one of the research directions of underwater real-time obstacle avoidance algorithm in the future
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[1] 李硕, 刘健, 徐会希, 等. 我国深海自主水下机器人的研究现状[J]. 中国科学: 信息科学, 2018, 48(9): 1152-1164.Li Shuo, Liu Jian, Xu Hui-xi, et al. Research Status of Deep-Sea Autonomous Underwater Robot in China[J]. Chinese Science: Information Science, 2018, 48(9): 1152-1164. [2] 钟宏伟, 李国良, 宋林桦, 等. 国外大型无人水下航行器发展综述[J]. 水下无人系统学报, 2018, 26(4): 273- 282.Zhong Hong-wei, Li Guo-liang, Song Lin-hua, et al. A Review of the Development of Large-Scale Unmanned Underwater Vehicles Abroad[J]. Journal of Unmanned Undersea Systems, 2018, 26(4): 273-282. [3] Han G, Long X, Zhu C, et al. A High-Availability Data Collection Scheme based on Multi-AUVs for Underwater Sensor Networks[J]. IEEE Transactions on Mobile Computing, 2020, 19(5): 1010-1022. [4] LI D L, Wang P, Du L. Path Planning Technologies for Autonomous Underwater Vehicles-A Review[J]. IEEE Access, 2019, 7: 9745-9768. [5] 潘光, 宋保维, 黄桥高, 等. 水下无人系统发展现状及其关键技术[J]. 水下无人系统学报, 2017, 25(2): 44-51.Pan Guang, Song Bao-wei, Huang Qiao-gao, et al. De-velopment Status and Key Technologies of Underwater Unmanned System[J]. Journal of Unmanned Undersea System, 2017, 25(2): 44-51. [6] Li J, Lee M, Lee W, et al. Real Time Obstacle Detection in a Water Tank Environment and its Experimental Study[C]//2014 IEEE/OES Autonomous Underwater Vehicles(AUV). Oxford, MS, USA: IEEE, 2014: 1-5. [7] Xidias E, Zissis D. Real Time Autonomous Maritime Navigation Using Dynamic Visibility Graphs[C]//2018 IEEE/OES Autonomous Underwater Vehicle Workshop (AUV). Porto, Portugal: IEEE, 2018: 1-6. [8] Huang C, Huang B, Zhang Y. An Improved A* Algorithm Applied to Three-Dimensional Space[C]//2019 IEEE 8th Jo- int International Information Technology and Artificial Intelligence Conference(ITAIC). Chongqing, China: IEEE, 2019. [9] Lee J, Kim D W. An Effective Initialization Method for Genetic Algorithm-Based Robot Path Planning Using a Directed Acyclic Graph[J]. Information Sciences, 2016, 332: 1-18. [10] Montiel O, Sepulveda R, Orozco-Rosas U. Optimal Path Planning Generation for Mobile Robots Using Parallel Evolutionary Artificial Potential Field[J]. Journal of Intelligent & Robotic Systems, 2015, 79(2): 237-257. [11] 杨健, 孟凡尘. 基于人工势场法的微小型AUV避障运动方法研究[J]. 水雷战与舰船防护, 2017, 25(4): 67-71.Yang Jian, Meng Fan-chen. Research on Obstacle Avoidance Method of Micro AUV Based on Artificial Potential Field Method[J]. Mine Warfare and Ship Protection, 2017, 25(4): 67-71. [12] 李东方, 王超, 邓宏彬, 等. 基于人工势场和RRT算法的机器蛇水下三维避障算法[J]. 兵工学报, 2017, 38(S1): 205-214.Li Dong-fang, Wang Chao, Deng Hong-bin, et al. Three Dimensional Obstacle Avoidance Algorithm Based on Artificial Potential Field and RRT Algorithm[J]. Journal of Military Engineering, 2017, 38(S1): 205-214. [13] 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, 41(12): 1421-1423.Wu Zheng-ping, Tang Nian, Chen Yong-liang, et al. AUV Path Planning Based on Improved Artificial Potential Field Method[J]. Chemical automation and instrumentation, 2014, 41(12): 1421-1423. [14] 程志, 张志安, 李金芝, 等. 改进人工势场法的移动机器人路径规划[J]. 计算机工程与应用, 2019, 55(23): 1-6.Cheng Zhi, Zhang Zhi-an, Li Jin-zhi, et al. Path Planning of Mobile Robot Based on Improved Artificial Potential Field Method[J]. Computer Engineering and Application, 2019, 55(23): 1-6. [15] Duan H B, Huang L Z. Imperialist Competitive Algorithm Optimized Artificial Neural Networks for UCAV Global Path Planning[J]. Neurocomputing, 2014, 125: 166-171. [16] 林政, 吕霞付. 基于改进模糊算法的水面无人艇自主避障[J]. 计算机应用, 2019, 39(9): 1-7.Lin Zheng, Lü Xia-fu. Autonomous Obstacle Avoidance of Surface Unmanned Vehicle Based on Improved Fuzzy Algorithm[J]. Computer application, 2019, 39(9): 1-7. [17] 张禹, 邢志伟, 黄俊峰, 等. 远程自治水下机器人三维实时避障方法研究[J]. 机器人, 2003, 25(6): 481-485.Zhang Yu, Xing Zhi-wei, Huang Jun-feng, et al. Research on 3D Real-Time Obstacle Avoidance Method of Autonomous Underwater Vehicle[J]. Robot, 2003, 25(6): 481-485. [18] 韩伟, 孙凯彪. 基于模糊人工势场法的智能全向车路径规划[J]. 计算机工程与应用, 2018, 54(6): 105-109, 167.Han Wei, Sun Kai-biao. Intelligent Omnidirectional Vehicle Path Planning Based on Fuzzy Artificial Potential Field Method[J]. Computer Engineering and Application, 2018, 54(6): 105-109, 167. [19] 张汝波, 李建军, 杨玉. 基于改进蚁群算法的AUV航路避障任务规划[J]. 华中科技大学学报(自然科学版), 2015, 43(S1): 428-430.Zhang Ru-bo, Li Jian-jun, Yang Yu. AUV Route Obstacle Avoidance Task Planning Based on Improved Ant Colony Algorithm[J]. Journal of Huazhong University of Science and Technology(Natural Science Edition), 2015, 43(S1): 428-430. [20] 马焱, 肖玉杰, 陈轶, 等. 基于改进烟花-蚁群算法的海流环境下水下无人潜航器的避障路径规划[J]. 导航与控制, 2019, 18(1): 51-59.Ma Yi, Xiao Yu-jie, Chen Yi, et al. Obstacle Avoidance Path Planning of Underwater Vehicle Based on Improved Fireworks Ant Colony Algorithm[J]. Navigation and Control, 2019, 18(1): 51-59. [21] 陈文渊, 沈斌坚. 基于三维成像声纳技术的AUV前视避障方法[J]. 传感器与微系统, 2015, 34(4): 12-15.Chen Wen-yuan, Shen Bin-jian. Obstacle Avoidance Method of AUV Based on 3D Imaging Sonar Technology[J]. Sensors and Microsystems, 2015, 34(4): 12-15. [22] 段群杰. 水下机器人实时路径规划方法研究[D]. 哈尔滨: 哈尔滨工程大学, 2007. [23] Endo G, Togawa K, Hirose S. Study on Self-Contained and Terrain Adaptive Active Cord Mechanism[C]//Proceedings of the 1999 IEEE/RSJ International Conference on Intelligent Robots and Systems(Cat. No.99CH36289). Kyongju, South Korea: IEEE, 1999: 1399-1405. [24] Dijkstra E W. A Note on Two Problems in Connection with Graphs[J]. Numerische Mathematics, 1959, 1(1): 269-271. [25] Hart P E, Nilsson N J, Raphael B. A Formal Basis for the Heuristic Determination of Minimum Cost Paths[J]. IEEE Transactions on Systems Science and Cybernetics, 1968, 4(2): 100-107. [26] Howden W E. Solution Plans and Interactive Problem Solving[J]. Computers & Graphics, 1975, 1(1): 21-26. [27] Lozano-Pérez T, Wesley M A. An Algorithm for Planning Collision-Free Paths among Polyhedral Obstacles[J]. Communications of the Acm, 1979, 22(10): 560-570. [28] Gowda I, Kirkpatrick D, Lee D, et al. Dynamic Voronoi diagrams[J]. IEEE Transactions on Information Theory, 1983, 29(5): 724-731. [29] Takahashi O, Schilling R J. Motion Planning in a Plane Using Generalized Voronoi Diagrams[J]. IEEE Transactions on Robotics and Automation, 1989, 5(2): 143-150. [30] Liu Y H, Arimoto S. Finding the Shortest Path of a Disc Among Polygonal Obstacles Using a Radius-Independent Graph[J]. IEEE Transactions on Robotics and Automation, 1995, 11(5): 682-691. [31] Khatib O. Real-time Obstacle Avoidance for Manipulators and Mobile Robots[J]. The International Journal of Robotics Research, 1986, 5(1): 90-98. [32] Borenstein J, Koren Y. The Vector Field Histogram-Fast Obstacle Avoidance for Mobile Robots[J]. IEEE Transactions on Robotics & Automation, 2002, 7(3): 278-288. [33] Ulrich I, Borenstein J. VFH+: Reliable Obstacle Avoidance for Fast Mobile Robots[C]//Proceedings of the 1998 IEEE International Conference on Robotics and Automation. Leuven, Belgium: IEEE, 1998: 1572-1577. [34] Kennedy J, Eberhart R. Particle Swarm Optimization[C]// Proceedings of ICNN’95-International Conference on Neural Networks. Perth, WA, Australia: IEEE, 1995: 1942-1948. [35] Kuffner J J, Lavalle S M. RRT-connect: An Efficient Ap-proach to Single-Query Path Planning[J]. IEEE International Conference on Robotics & Automation, 2000, 2(1): 995-1001. [36] 高云峰, 黄海. 复杂环境下基于势场原理的路径规划方法[J]. 机器人, 2004, 26(2): 114-118.Gao Yun-feng, Huang Hai. Path Planning Method Based on Potential Field Principle in Complex Environment[J]. Robot, 2004, 26(2): 114-118. [37] 刘学敏, 李英辉, 徐玉如. 基于运动平衡点的水下机器人自主避障方式[J]. 机器人, 2001, 23(3): 270-274.Liu Xue-min, Li Ying-hui, Xu Yu-ru. Autonomous Obstacle Avoidance of Underwater Robot Based on Motion Balance Point[J]. Robot, 2001, 23(3): 270-274. [38] 孙玉山, 张英浩, 常文田, 等. 基于改进运动平衡点的水下机器人自主避障方法研究[J]. 中国造船, 2013, 54 (2): 17-25.Sun Yu-shan, Zhang Ying-hao, Chang Wen-tian, et al. Research on Autonomous Obstacle Avoidance Method of Underwater Robot Based on Improved Motion Balance Point[J]. China Shipbuilding, 2013, 54(2): 17-25. [39] 李东方, 李科伟, 邓宏彬, 等. 基于人工势场与IB- LBM的机器蛇水中2D避障控制算法[J]. 机器人, 2018, 40(3): 346-359.Li Dong-fang, Li Ke-wei, Deng Hong-bin, et al. 2D Obstacle Avoidance Control Algorithm in Snake Water Based on Artificial Potential Field and IB-LBM[J]. Robot, 2018, 40(3): 346-359. [40] 李沛伦, 杨启. 基于改进人工势场法的水下滑翔机路径规划[J]. 舰船科学技术, 2019, 41(7): 89-93.Li Pei-lun, Yang Qi. Underwater Glider Path Planning Based on Improved Artificial Potential Field Method[J]. Ship Science and Technology, 2019, 41(7): 89-93. [41] 王奎民, 赵玉飞, 侯恕萍, 等. 一种改进人工势场的UUV动碍航物规避方法[J]. 智能系统学报, 2014, 9(1): 47-52.Wang Kui-min, Zhao Yu-fei, Hou Shu-ping, et al. A Method to Improve UUV Obstacle Avoidance of Artificial Potential Field[J]. Journal of Intelligent Systems, 2014, 9(1): 47-52. [42] 姚鹏, 解则晓. 基于修正导航向量场的AUV自主避障方法[J/OL]. 自动化学报. https://kns.cnki.net/KCMS/ detail/11.2109.TP.20190219.1551.005.html, 2019-02-19.Yao Peng, Jie Ze-xiao. AUV Autonomous Obstacle Avoidance Method Based on Modified Navigation Vector Field[J]. Acta Automatica Sinica. https://kns.cnki.net/KC MS/detail/11. 2109.TP.20190219.1551.005.html, 2019-02-19. [43] Anwary A R, Lee Y, Jung H, et al. Unsupervised Real-time Obstacle Avoidance Technique Based on a Hybrid Fuzzy Method for AUVs[J]. International Journal of Fuzzy Logic and Intelligent Systems, 2008, 8(1): 82-86. [44] Sun B, Zhu D Q, Jiang L S, et al. A Novel Fuzzy Control Algorithm for Three-Dimensional AUV Path Planning Based on Sonar Model[J]. Journal of Intelligent & Fuzzy Systems, 2014, 26(6): 2913-2926. [45] Sun B, Zhu D Q, Yang S X. An Optimized Fuzzy Control Algorithm for Three-Dimensional AUV Path Planning[J]. International Journal of Fuzzy Systems, 2018, 20(2): 597-610. [46] Sahu B K, Subudhi B. Flocking Control of Multiple AUVs Based on Fuzzy Potential Functions[J]. IEEE Transactions on Fuzzy Systems, 2018, 26(5): 2539-2551. [47] Ghatee M, Mohades A. Motion Planning in Order to Optimize the Length and Clearance Applying a Hopfield Neural Network[J]. Expert Systems with Applications, 2009, 36: 4688-4695. [48] Tavares J. Bio-inspired Algorithms for the Vehicle Routing Problem(Studies in Computational Intelligence)[M]. Berlin: Springer, 2009. [49] Yan M Z, Zhu D Q. An Algorithm of Complete Coverage Path Planning for Autonomous Underwater Vehicles[J]. Key Engineering Materials, 2011, 467-469: 1377-1385. [50] Li S, Guo Y. Neural-network based AUV Path Planning in Estuary Environments[C]//Proceedings of the 10th World Congress on Intelligent Control and Automation. Beijing, China: 10th World Congress on Intelligent Control and Automation, 2012: 3724-3730. [51] 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): 798-806.Zhu Da-qi, Sun Bing, Li Li. AUV 3D Autonomous Path Planning and Obstacle Avoidance Algorithm Based on Bioheuristic Model[J]. Control and decision, 2015, 30(5): 798-806. [52] Huang Z R, Zhu D Q, Sun B. A Multi-AUV Cooperative Hunting Method in 3-D Underwater Environment with Obstacle[J]. Engineering Applications of Artificial Intel- ligence, 2016, 50: 192-200. [53] 朱大奇, 刘雨, 孙兵, 等. 自治水下机器人的自主启发式生物启发神经网络路径规划算法[J]. 控制理论与应用, 2019, 36(2): 183-191.Zhu Da-qi, Liu Yu, Sun Bing, et al. Autonomous Heuristic Bioheuristic Neural Network Path Planning Algorithm for Autonomous Underwater Vehicles[J]. Control Theory and Application, 2019, 36(2): 183-191. [54] Liu H, Xu B, Lu D J, et al. A Path Planning Approach for Crowd Evacuation in Buildings Based on Improved Artificial Bee Colony Algorithm[J]. Applied Sofit Computing, 2018, 68: 360-376. [55] Wei K, Ren B Y. A Method on Dynamic Path Planning for Robotic Manipulator Autonomous Obstacle Avoidance Based on an Improved RRT Algorithm[J]. Sensors, 2018, 18(2): 571. [56] Patle B K, Pandey A, Jagadeesh A, et al. Path Planning in Uncertain Environment by Using Firefly Algorithm[J]. Defence Technology, 2018(14): 691-701. [57] Yao P, Zhao S. Three-Dimensional Path Planning for AUV Based on Interfered Fluid Dynamical System Under Ocean Current[J]. IEEE Access, 2018, 6: 42904-42916. [58] 孙兵, 朱大奇, 杨元元. 基于粒子群优化的自治水下机器人模糊路径规划[J]. 高技术通讯, 2013, 23(12): 1284-1291.Sun Bing, Zhu Da-qi, Yang Yuan-yuan. Fuzzy Path Planning of Autonomous Underwater Vehicle Based on Particle Swarm Optimization[J]. High Tech Communication, 2013, 23(12): 1284-1291. [59] 罗颀栋. 水下球形机器人的运动控制方法研究[D]. 北京: 北京邮电大学, 2017. [60] 田广, 刘和祥, 赵海鹰, 等. 自治式水下机器人避障行为机制研究[J]. 传感器与微系统, 2009, 28(12): 59-63.Tian Guang, Liu He-xiang, Zhao Hai-ying, et al. Research on Obstacle Avoidance Mechanism of Autonomous Underwater Vehicle[J]. Sensors and Microsystems, 2009, 28 (12): 59-63.
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