2.624

2020影响因子

(CJCR)

  • 中文核心
  • EI
  • 中国科技核心
  • Scopus
  • CSCD
  • 英国科学文摘

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于修正导航向量场的AUV自主避障方法

姚鹏 解则晓

姚鹏, 解则晓. 基于修正导航向量场的AUV自主避障方法. 自动化学报, 2020, 46(8): 1670−1680 doi: 10.16383/j.aas.c180219
引用本文: 姚鹏, 解则晓. 基于修正导航向量场的AUV自主避障方法. 自动化学报, 2020, 46(8): 1670−1680 doi: 10.16383/j.aas.c180219
Yao Peng, Xie Ze-Xiao. Autonomous obstacle avoidance for AUV based on modified guidance vector field. Acta Automatica Sinica, 2020, 46(8): 1670−1680 doi: 10.16383/j.aas.c180219
Citation: Yao Peng, Xie Ze-Xiao. Autonomous obstacle avoidance for AUV based on modified guidance vector field. Acta Automatica Sinica, 2020, 46(8): 1670−1680 doi: 10.16383/j.aas.c180219

基于修正导航向量场的AUV自主避障方法

doi: 10.16383/j.aas.c180219
基金项目: 

山东省自然科学基金 ZR2018BF016

中国博士后科学基金 2017M622278

中央高校基本科研业务费 201713046

详细信息
    作者简介:

    解则晓  中国海洋大学工程学院教授.主要研究方向为机器视觉与水下三维测量. E-mail: xiezexiao@ouc.edu.cn

    通讯作者:

    姚鹏  中国海洋大学工程学院讲师.主要研究方向为无人系统路径规划与智能决策, 多机器人协同优化与控制.本文通信作者.E-mail: yaopenghappy@163.com

Autonomous Obstacle Avoidance for AUV Based on Modified Guidance Vector Field

Funds: 

Natural Science Foundation of China ZR2018BF016

China Postdoctoral Science Foundation 2017M622278

Fundamental Research Funds for the Central Universities 201713046

More Information
    Author Bio:

    XIE Ze-Xiao Professor at the College of Engineering, Ocean University of China. His research interest covers machine vision and underwater 3D measurement technology

    Corresponding author: YAO Peng Lecturer at the College of Engineering, Ocean University of China. His research interest covers path planning and intelligent decision of unmanned system, cooperative optimization and control of multi-robots. Corresponding author of this paper
  • 摘要: 针对复杂海洋环境下的自治水下机器人(Autonomous underwater vehicle, AUV)三维避障问题, 本文提出了一种高效的修正导航向量场方法.构建自由空间下的初始导航向量场, 引导AUV以最短路径向目标点航行.定义修正矩阵来量化描述障碍物对初始导航向量场的影响, 得到障碍空间下的修正导航向量场, 使得AUV向目标点航行的同时躲避静态障碍.通过结合障碍物运动速度, 分别构建相对初始导航向量场与相对修正导航向量场, 并采取有限时域推演与调整策略, 最终引导AUV安全躲避动态障碍.仿真结果表明, 本方法能较好地应用于复杂海洋环境下的AUV避障任务.
  • 图  1  速度矢量关系图

    Fig.  1  Relationship between velocity vectors

    图  2  海洋环境下的典型凸面体障碍物

    Fig.  2  Convex obstacles in ocean environment

    图  3  AUV自主避障示意图

    Fig.  3  Illustration of AUV avoiding obstacles

    图  4  导航向量场示意图

    Fig.  4  Illustration of guidance vector fields

    图  5  AUV躲避圆球障碍物

    Fig.  5  AUV avoiding a sphere obstacle

    图  6  AUV躲避凹陷区域

    Fig.  6  AUV avoiding concave area

    图  7  AUV躲避动态障碍物

    Fig.  7  AUV avoiding dynamic obstacles

    图  8  AUV部分状态量与控制输入

    Fig.  8  AUV state value and control input

    图  9  复杂场景下AUV自主避障

    Fig.  9  AUV avoiding obstacles in a complex scenario

    表  1  APF与MGVF方法的量化指标对比

    Table  1  Performance indicators of APF and MGVF

    方法 $L {\rm{(m)}}$ $GS (^\circ)$ $LS (^\circ)$ $d_{ \text{{AUV- obs}}}^{\min } {\rm{(m)}}$
    APF 488 2.08 14.11 0.3
    MGVF 453 1.14 5.56 5.6
    下载: 导出CSV
  • [1] Wynn R B, Huvenne V A I, Le Bas T P, Murton B J, Connelly D P, Bett B J, et al. Autonomous underwater vehicles (AUVs): Their past, present and future contributions to the advancement of marine geoscience. Marine Geology, 2014, 352: 451-468 doi: 10.1016/j.margeo.2014.03.012
    [2] 谭民, 王硕.机器人技术研究进展.自动化学报, 2013, 39(7): 963-972 doi: 10.3724/SP.J.1004.2013.00963

    Tan Min, Wang Shuo. Research progress on robotics. Acta Automatica Sinica, 2013, 39(7): 963-972 doi: 10.3724/SP.J.1004.2013.00963
    [3] Zeng Z, Lian L, Sammut K, He F P, Tang Y L, Lammas A. A survey on path planning for persistent autonomy of autonomous underwater vehicles. Ocean Engineering, 2015, 110: 303-313 doi: 10.1016/j.oceaneng.2015.10.007
    [4] Yao P, Wang H L, Su Z K. Real-time path planning of unmanned aerial vehicle for target tracking and obstacle avoidance in complex dynamic environment. Aerospace Science and Technology, 2015, 47: 269-279 doi: 10.1016/j.ast.2015.09.037
    [5] 申浩宇, 吴洪涛, 陈柏, 丁力, 杨小龙.基于主从任务转化的冗余度机器人避障算法.机器人, 2014, 36(4): 425-429 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=jqr201404006

    Shen Hao-Yu, Wu Hong-Tao, Chen Bo, Ding Li, Yang Xiao-Long. Obstacle avoidance algorithm for redundant robots based on transition between the primary and secondary tasks. Robot, 2014, 36(4): 425-429 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=jqr201404006
    [6] Fang M C, Wang S M, Wu M C, Lin Y H. Applying the self-tuning fuzzy control with the image detection technique on the obstacle-avoidance for autonomous underwater vehicles. Ocean Engineering, 2015, 93: 11-24 doi: 10.1016/j.oceaneng.2014.11.001
    [7] Kamil F, Tang S H, Khaksar W, Zulkifli N, Ahmad S A. A review on motion planning and obstacle avoidance approaches in dynamic environments. Advances in Robotics and Automation, 2015, 4(2): 1000134 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=10.1177/0011000082101002
    [8] Garrido S, Moreno L, Abderrahim M, Martin F. Path planning for mobile robot navigation using voronoi diagram and fast marching. In: Proceedings of the 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems. Beijing, China: IEEE, 2006. 2376-2381
    [9] Sgorbissa A, Zaccaria R. Planning and obstacle avoidance in mobile robotics. Robotics and Autonomous Systems, 2012, 60(4): 628-638 doi: 10.1016/j.robot.2011.12.009
    [10] 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.控制与决策, 2015, 30(5): 798-806 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=kzyjc201505004

    Zhu Da-Qi, Sun Bing, Li Li. Algorithm for AUV's 3-D path planning and safe obstacle avoidance based on biological inspired model. Control and Decision, 2015, 30(5): 798-806 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=kzyjc201505004
    [11] Carroll K P, McClaran S R, Nelson E L, Barnett D M, Friesen D K, William G N. AUV path planning: An $A^{\ast}$ approach to path planning with consideration of variable vehicle speeds and multiple, overlapping, time-dependent exclusion zones. In: Proceedings of the 1992 Symposium on Autonomous Underwater Vehicle Technology. Washington, USA: IEEE, 1992. 79-84
    [12] 严浙平, 赵玉飞, 陈涛.多约束条件下UUV空间航迹规划.鱼雷技术, 2011, 19(5): 365-369, 375 doi: 10.3969/j.issn.1673-1948.2011.05.009

    Yan Zhe-Ping, Zhao Yu-Fei, Chen Tao. Three-dimensional path planning for UUV with multiple constraints. Torpedo Technology, 2011, 19(5): 365-369, 375 doi: 10.3969/j.issn.1673-1948.2011.05.009
    [13] Hernández J D, Vidal E, Vallicrosa G, Galceran E, Carreras M. Online path planning for autonomous underwater vehicles in unknown environments. In: Proceedings of the 2015 IEEE International Conference on Robotics and Automation. Washington, USA: IEEE, 2015. 1152-1157
    [14] McMahon J, Plaku E. Mission and motion planning for autonomous underwater vehicles operating in spatially and temporally complex environments. IEEE Journal of Oceanic Engineering, 2016, 41(4): 893-912 doi: 10.1109/JOE.2015.2503498
    [15] Ademoye T A, Davari A, Cao W. Three dimensional obstacle avoidance maneuver planning using mixed integer linear programming. In: Proceedings of the 12th IASTED International Conference on Robotics and Applications. Honolulu, USA: ACTA Press, 2006. 180-183
    [16] Chen D S, Batson R G, Dang Y. Applied Integer Programming: Modeling and Solution. Hoboken, New Jersey: John Wiley and Sons, 2010.
    [17] Saravanakumar S, Asokan T. Multipoint potential field method for path planning of autonomous underwater vehicles in 3D space. Intelligent Service Robotics, 2013, 6(4): 211-224 doi: 10.1007/s11370-013-0138-2
    [18] Braginsky B, Guterman H. Obstacle avoidance approaches for autonomous underwater vehicle: Simulation and experimental results. IEEE Journal of Oceanic Engineering, 2016, 41(4): 882-892 doi: 10.1109/JOE.2015.2506204
    [19] Li S H, Wang X Y. Finite-time consensus and collision avoidance control algorithms for multiple AUVs. Automatica, 2013, 49(11): 3359-3367 doi: 10.1016/j.automatica.2013.08.003
    [20] Waydo S, Murray R M. Vehicle motion planning using stream functions. In: Proceedings of the 2003 IEEE International Conference on Robotics and Automation. Taipei, China: IEEE, 2003. 2484-2491
    [21] Wang H L, Lyu W T, Yao P, Liang X, Liu C. Three-dimensional path planning for unmanned aerial vehicle based on interfered fluid dynamical system. Chinese Journal of Aeronautics, 2015, 28(1): 229-239 doi: 10.1016/j.cja.2014.12.031
    [22] 姚鹏, 王宏伦.基于改进流体扰动算法与灰狼优化的无人机三维航路规划.控制与决策, 2016, 31(4): 701-708 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=kzyjc201604019

    Yao Peng, Wang Hong-Lun. Three-dimensional path planning for UAV based on improved interfered fluid dynamical system and grey wolf optimizer. Control and Decision, 2016, 31(4): 701-708 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=kzyjc201604019
    [23] Kim S, Oh H, Tsourdos A. Nonlinear model predictive coordinated standoff tracking of a moving ground vehicle. Journal of Guidance, Control, and Dynamics, 2013, 36(2): 557-566 doi: 10.2514/1.56254
    [24] Nadarajah N, Tharmarasa R, McDonald M, Kirubarajan T. IMM forward filtering and backward smoothing for maneuvering target tracking. IEEE Transactions on Aerospace and Electronic Systems, 2012, 48(3): 2673-2678 doi: 10.1109/TAES.2012.6237617
  • 加载中
图(9) / 表(1)
计量
  • 文章访问数:  927
  • HTML全文浏览量:  122
  • PDF下载量:  232
  • 被引次数: 0
出版历程
  • 收稿日期:  2018-04-16
  • 录用日期:  2018-09-21
  • 刊出日期:  2020-08-26

目录

    /

    返回文章
    返回