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移动机器人自主动态避障方法

张贺 缪存孝 唐友军 闫晓强 时彦洋 余远金

张贺, 缪存孝, 唐友军, 等 . 移动机器人自主动态避障方法[J]. 北京航空航天大学学报, 2022, 48(6): 1013-1021. doi: 10.13700/j.bh.1001-5965.2020.0727
引用本文: 张贺, 缪存孝, 唐友军, 等 . 移动机器人自主动态避障方法[J]. 北京航空航天大学学报, 2022, 48(6): 1013-1021. doi: 10.13700/j.bh.1001-5965.2020.0727
ZHANG He, MIAO Cunxiao, TANG Youjun, et al. Dynamic obstacle avoidance method for mobile robots[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(6): 1013-1021. doi: 10.13700/j.bh.1001-5965.2020.0727(in Chinese)
Citation: ZHANG He, MIAO Cunxiao, TANG Youjun, et al. Dynamic obstacle avoidance method for mobile robots[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(6): 1013-1021. doi: 10.13700/j.bh.1001-5965.2020.0727(in Chinese)

移动机器人自主动态避障方法

doi: 10.13700/j.bh.1001-5965.2020.0727
基金项目: 

国家重点研发计划 2018YFC0810500

国家自然科学基金 60277005

北京理工大学青年教师学术启动计划 2020CX04104

详细信息
    通讯作者:

    缪存孝, E-mail: miaocx@ustb.edu.cn

  • 中图分类号: TP242.6

Dynamic obstacle avoidance method for mobile robots

Funds: 

National Key R & D Program of China 2018YFC0810500

National Natural Science Foundation of China 60277005

Beijing Institute of Technology Research Fund Program for Young Scholars 2020CX04104

More Information
  • 摘要:

    针对基于水流场的全向移动机器人动态避障过程中可能出现的避障失效和避障路径过长问题, 提出了一种基于速度斥力场改进人工势场方法的全向移动机器人自主动态避障方法。详细分析基于水流场改进的人工势场法在动态避障过程中存在的移动机器人从障碍物前方绕过而引起的避障路径过长或避障失效问题;在基于水流场的人工势场避障方法中, 根据移动机器人和动态障碍物的相对速度, 引入速度斥力场, 使得移动机器人从动态障碍物的后方通过, 实现全向移动机器人的安全自主动态避障。通过仿真和室内避障实验, 验证了所提自主动态避障方法的有效性和实用性。

     

  • 图 1  障碍物位置与水流斥力场方向的关系

    Figure 1.  Relation between position of obstacles and direction of repulsive force in flow field

    图 2  力和运动示意图

    Figure 2.  Schematic diagram of force and motion

    图 3  速度斥力场示意图

    Figure 3.  Schematic diagram of velocity repulsion field

    图 4  移动机器人在合力势场中的受力示意图

    Figure 4.  Schematic diagram of forces on mobile robot in resultant force potential field

    图 5  方案A关键时刻仿真曲线

    Figure 5.  Key moments simulation curves of Plan A

    图 6  方案B关键时刻仿真曲线

    Figure 6.  Key moments simulation curves of Plan B

    图 7  移动机器人在方案A中的仿真受力

    Figure 7.  Force diagram during simulation of Plan A of mobile robot

    图 8  移动机器人在方案B中的仿真受力

    Figure 8.  Force diagram during simulation of Plan B of mobile robot

    图 9  实验平台

    Figure 9.  Experiment platform

    图 10  方案A关键时刻实验示意图

    Figure 10.  Schematic diagram of experiment results at key moments of Plan A

    图 11  方案B关键时刻实验示意图

    Figure 11.  Schematic diagram of experiment results at key moments of Plan B

    图 12  移动机器人在方案A中的实验受力

    Figure 12.  Force diagram during experiment of Plan A of mobile robot

    图 13  移动机器人在方案B中的实验受力

    Figure 13.  Force diagram during experiment of Plan B of mobile robot

    表  1  算法参数

    Table  1.   Algorithm parameters

    参数 数值
    引力系数Katt 10
    斥力系数Krep 30
    速度斥力场系数Kv 40
    待优化参数n 2
    速度斥力场影响半径ρobv 1.5
    下载: 导出CSV

    表  2  仿真对比

    Table  2.   Simulation comparison

    参数 方案A 方案B
    路径长度 32.8 29.6
    仿真运行时间/s 16.4 14.8
    下载: 导出CSV
  • [1] 庞师坤, 梁晓锋, 李英辉, 等. 基于零空间行为法的自主水下机器人避障策略[J]. 上海交通大学学报, 2020, 54(3): 295-304. https://www.cnki.com.cn/Article/CJFDTOTAL-SHJT202003010.htm

    PANG S K, LIANG X F, LI Y H, et al. Collision avoidance strategy for autonomous underwater vehicle based on null-space-based behavioral approach[J]. Journal of Shanghai Jiao Tong University, 2020, 54(3): 295-304(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-SHJT202003010.htm
    [2] 付丽霞, 任玉洁, 张勇, 等. 基于改进平滑A*算法的移动机器人路径规划[J]. 计算机仿真, 2020, 37(8): 271-276. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJZ202008058.htm

    FU L X, REN Y J, ZHANG Y, et al. Path planning of mobile robot based on improved smoothing A* algorithms[J]. Computer Simulation, 2020, 37(8): 271-276(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-JSJZ202008058.htm
    [3] MONTIEL O, OROZCO-ROSAS U, SEP U ' LVEDA R. Path planning for mobile robots using bacterial potential field for avoiding static and dynamic obstacles[J]. Expert Systems with Applications, 2015, 42(12): 5177-5191. doi: 10.1016/j.eswa.2015.02.033
    [4] 杨明辉, 吴垚, 张勇, 等. 室内动态环境下的移动机器人自主避障策略[J]. 中南大学学报(自然科学版), 2019, 50(8): 1833-1839. https://www.cnki.com.cn/Article/CJFDTOTAL-ZNGD201908010.htm

    YANG M H, WU Y, ZHANG Y, et al. Autonomous obstacle avoidance strategy for mobile robots in indoor dynamic environment[J]. Journal of Central South University (Science and Technology), 2019, 50(8): 1833-1839(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-ZNGD201908010.htm
    [5] 李卫硕, 孙剑, 陈伟. 基于BP神经网络机器人实时避障算法[J]. 仪器仪表学报, 2019, 40(11): 204-211. https://www.cnki.com.cn/Article/CJFDTOTAL-YQXB201911025.htm

    LI W S, SUN J, CHEN W. Real-time obstacle avoidance algorithm for robots based on BP neural network[J]. Chinese Journal of Scientific Instrument, 2019, 40(11): 204-211(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-YQXB201911025.htm
    [6] RAMEZANI D A, LEE D J. An end-to-end deep reinforcement learning-based intelligent agent capable of autonomous exploration in unknown environments[J]. Sensors, 2018, 18(10): 3575. doi: 10.3390/s18103575
    [7] 庞磊, 曹志强, 喻俊志. 基于A*和TEB融合的行人感知无碰跟随方法[J]. 航空学报, 2021, 42(4): 524909. https://www.cnki.com.cn/Article/CJFDTOTAL-HKXB202104036.htm

    PANG L, CAO Z Q, YU J Z. A pedestrian-aware collision-free following approach for mobile robots based on A* and TEB[J]. Acta Aeronautica et Astronautica Sinica, 2021, 42(4): 524909(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-HKXB202104036.htm
    [8] 槐创锋, 郭龙, 贾雪艳, 等. 改进A*算法与动态窗口法的机器人动态路径规划[J]. 计算机工程与应用, 2021, 57(8): 244-248. https://www.cnki.com.cn/Article/CJFDTOTAL-JSGG202108034.htm

    HUAI C F, GUO L, JIA X Y, et al. Improved A* algorithm and dynamic window method for robot dynamic path planning[J]. Computer Engineering and Applications, 2021, 57(8): 244-248(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-JSGG202108034.htm
    [9] 程志, 张志安, 乐伟扬, 等. 基于D* Lite算法的三维路径规划研究[J]. 传感器与微系统, 2020, 39(12): 71-73. https://www.cnki.com.cn/Article/CJFDTOTAL-CGQJ202012021.htm

    CHENG Z, ZHANG Z A, YUE W Y, et al. Research on path planning in 3D terrain based on D* Lite algorithm[J]. Transducer and Microsystem Technologies, 2020, 39(12): 71-73(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-CGQJ202012021.htm
    [10] 徐开放. 基于D*Lite算法的移动机器人路径规划研究[D]. 哈尔滨: 哈尔滨工业大学, 2017.

    XU K F. Research on mobile robot path-planning based on D* Lite algorithm[D]. Harbin: Harbin Institute of Technology, 2017(in Chinese).
    [11] 张贺, 胡越黎, 王权, 等. 基于改进D*算法的移动机器人路径规划[J]. 工业控制计算机, 2016, 29(11): 73-74. https://www.cnki.com.cn/Article/CJFDTOTAL-GYKJ201611035.htm

    ZHANG H, HU Y L, WANG Q, et al. Path planning of mobile robot based on improved D* algorithm[J]. Industrial Control Computer, 2016, 29(11): 73-74(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-GYKJ201611035.htm
    [12] 朱战霞, 靖飒, 仲剑飞, 等. 基于碰撞检测的空间冗余机械臂避障路径规划[J]. 西北工业大学学报, 2020, 38(1): 183-190. https://www.cnki.com.cn/Article/CJFDTOTAL-XBGD202001023.htm

    ZHU Z X, JING S, ZHONG J F, et al. Obstacle avoidance path planning of space redundant manipulator based on a collision detection algorithm[J]. Journal of Northwestern Polytechnical University, 2020, 38(1): 183-190(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-XBGD202001023.htm
    [13] LIN Y H, HUANG L C, CHEN S Y, et al. The optimal route planning for inspection task of autonomous underwater vehicle composed of MOPSO-based dynamic routing algorithm in currents[J]. Applied Ocean Research, 2018, 75: 178-192.
    [14] 魏彤, 龙琛. 基于改进遗传算法的移动机器人路径规划[J]. 北京航空航天大学学报, 2020, 46(4): 703-711. doi: 10.13700/j.bh.1001-5965.2019.0298

    WEI T, LONG C. Path planning for mobile robot based on improved genetic algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(4): 703-711(in Chinese). doi: 10.13700/j.bh.1001-5965.2019.0298
    [15] 郭一聪, 刘小雄, 章卫国, 等. 基于改进势场法的无人机三维路径规划方法[J]. 西北工业大学学报, 2020, 38(5): 977-986. https://www.cnki.com.cn/Article/CJFDTOTAL-XBGD202005009.htm

    GUO Y C, LIU X X, ZHANG W G, et al. 3D path planning method for UAV based on improved artificial potential field[J]. Journal of Northwestern Polytechnical University, 2020, 38(5): 977-986(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-XBGD202005009.htm
    [16] 陈麒杰, 晋玉强, 王陶昱. 基于改进人工势场算法的无人机群避障算法研究[J]. 导航定位与授时, 2020, 7(6): 109-113. https://www.cnki.com.cn/Article/CJFDTOTAL-DWSS202006016.htm

    CHEN Q J, JIN Y Q, WANG T Y. Research on obstacle avoidance algorithm of UAV group based on improved artificial potential field algorithm[J]. Navigation Positioning and Timing, 2020, 7(6): 109-113(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-DWSS202006016.htm
    [17] 崔宝侠, 宋佳瑞. 未知环境下机器人避障及动态目标追踪[J]. 沈阳工业大学学报, 2018, 40(3): 292-298. https://www.cnki.com.cn/Article/CJFDTOTAL-SYGY201803010.htm

    CUI B X, SONG J R. Obstacle avoidance and dynamic target tracking of robot in unknown environment[J]. Journal of Shenyang University of Technology, 2018, 40(3): 292-298(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-SYGY201803010.htm
    [18] 杜婉茹, 王潇茵, 田涛, 等. 面向未知环境及动态障碍的人工势场路径规划算法[J]. 计算机科学, 2021, 48(2): 250-256. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJA202102033.htm

    DU W R, WANG X Y, TIAN T, et al. Artificial potential field path planning algorithm for unknown environment and dynamic obstacles[J]. Computer Science, 2021, 48(2): 250-256(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-JSJA202102033.htm
    [19] 张大志, 刘万辉, 缪存孝, 等. 全向移动机器人动态避障方法[J]. 北京航空航天大学学报, 2021, 47(6): 1115-1123. doi: 10.13700/j.bh.1001-5965.2020.0155

    ZHANG D Z, LIU W H, MIAO C X, et al. Dynamic obstacle avoidance method for omnidirectional mobile robots[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(6): 1115-1123(in Chinese). doi: 10.13700/j.bh.1001-5965.2020.0155
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出版历程
  • 收稿日期:  2021-01-02
  • 录用日期:  2021-04-20
  • 刊出日期:  2022-06-20

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