Volume 50 Issue 7
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Article Navigation >  Journal of Mine Automation  > 2024  >  50(7): 107-114
ZHU Hongbo, HUA Rong. Path planning method for coal mine inspection robot[J]. Journal of Mine Automation，2024，50（7）：107-114.  doi: 10.13272/j.issn.1671-251x.2024040033
Citation: ZHU Hongbo, HUA Rong. Path planning method for coal mine inspection robot[J]. Journal of Mine Automation，2024，50（7）：107-114.  doi: 10.13272/j.issn.1671-251x.2024040033
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Path planning method for coal mine inspection robot

doi: 10.13272/j.issn.1671-251x.2024040033

  • College of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China

  • Received Date: 2024-04-11
  • Rev Recd Date: 2024-07-10
    • Available Online: 2024-07-30
    Abstract
  • Path planning is a key technology for autonomous movement of inspection robot. The coal mine inspection robot has problems such as slow convergence speed and low search efficiency when planning paths using the rapidly-expanding random tree (RRT) algorithm. In order to solve the above problems, a combined force potential field guided RRT algorithm is proposed. The algorithm uses the repulsive force field in the combined force potential field to construct a dynamic step size. The coal mine inspection robot can adjust the step size near obstacles to improve the convergence speed of the algorithm. By utilizing the combined force field formed by the gravitational field in both the target node and random node directions, as well as the repulsive field generated by the nearest obstacle on the coal mine inspection robot, the generation direction of new nodes can be improved. It reduces the randomness of tree expansion and enhances the search efficiency of the algorithm. A pruning operation is performed on the paths planned based on the combined potential field guided RRT algorithm and smoothed using third-order Bessel curve. A simulation experiment is conducted in Matlab software on the path planning method of the coal mine inspection robot guided by the combined force potential field RRT algorithm. The results show that compared with the RRT algorithm and RRT* algorithm, the average path planning time of the combined potential field guided RRT algorithm in simple environments is reduced by 33.84% and 44.27%. The average path length is reduced by 15.29% and 4.42%, respectively. In complex environments, the average path planning time is reduced by 34.93% and 47.12%, and the average path length is reduced by 13.64% and 9.44%, respectively. In simulated coal mine environments, the average path planning time is reduced by 28.06% and 42.67%, and the average path length is reduced by 12.22% and 10.18%, respectively. After pruning and smoothing the path planned by the combined force potential field guided RRT algorithm, the number of turning points and angle changes in the path decrease, making the path smoother.

     

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    • References(21)
  • [1]
    蔡治华,周东旭,赵明辉. 煤矿巡检机器人控制系统设计[J]. 工矿自动化,2022,48(5):112-117.

    CAI Zhihua,ZHOU Dongxu,ZHAO Minghui. Design of coal mine inspection robot control system[J]. Journal of Mine Automation,2022,48(5):112-117.
    [2]
    姜媛媛,丰雪艳. 基于改进A*算法的煤矿救援机器人路径规划[J]. 工矿自动化,2023,49(8):53-59.

    JIANG Yuanyuan,FENG Xueyan. Path planning of coal mine rescue robot based on improved A* algorithm[J]. Journal of Mine Automation,2023,49(8):53-59.
    [3]
    李薪颖,单梁,常路,等. 复杂环境下基于多目标粒子群的DWA路径规划算法[J]. 国防科技大学学报,2022,44(4):52-59. doi: 10.11887/j.cn.202204006

    LI Xinying,SHAN Liang,CHANG Lu,et al. DWA path planning algorithm based on multi-objective particle swarm optimization in complex environment[J]. Journal of National University of Defense Technology,2022,44(4):52-59. doi: 10.11887/j.cn.202204006
    [4]
    ZHANG Yinyan,LI Shuai,GUO Hongliang. A type of biased consen sus-based distributed neural network for path planning[J]. Nonlinear Dynamics,2017,89:1803-1815.
    [5]
    李波,杨志鹏,贾卓然,等. 一种无监督学习型神经网络的无人机全区域侦察路径规划[J]. 西北工业大学学报,2021,39(1):77-84. doi: 10.3969/j.issn.1000-2758.2021.01.010

    LI Bo,YANG Zhipeng,JIA Zhuoran,et al. An unsupervised learning neural network for planning UAV full-area reconnaissance path[J]. Journal of Northwestern Polytechnical University,2021,39(1):77-84. doi: 10.3969/j.issn.1000-2758.2021.01.010
    [6]
    OROZCO-ROSAS U,OSCAR M,SEPULVEDA R. Mobile robot path planning using membrane evolutionary artificial potential field[J]. Applied Soft Computing,2019,77:236-251. doi: 10.1016/j.asoc.2019.01.036
    [7]
    翟丽,张雪莹,张闲,等. 基于势场法的无人车局部动态避障路径规划算法[J]. 北京理工大学学报,2022,42(7):696-705.

    ZHAI Li,ZHANG Xueying,ZHANG Xian,et al. Local dynamic obstacle avoidance path planning algorithm for unmanned vehicles based on potential field method[J]. Transactions of Beijing Institute of Technology,2022,42(7):696-705.
    [8]
    叶颖诗,魏福义,蔡贤资. 基于并行计算的快速Dijkstra算法研究[J]. 计算机工程与应用,2020,56(6):58-65. doi: 10.3778/j.issn.1002-8331.1903-0119

    YE Yingshi,WEI Fuyi,CAI Xianzi. Research on fast Dijkstra algorithm based on parallel computing[J]. Computer Engineering and Applications,2020,56(6):58-65. doi: 10.3778/j.issn.1002-8331.1903-0119
    [9]
    巩慧,倪翠,王朋,等. 基于Dijkstra算法的平滑路径规划方法[J]. 北京航空航天大学学报,2024,50(2):535-541.

    GONG Hui,NI Cui,WANG Peng,et al. A smooth path planning method based on Dijkstra algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics,2024,50(2):535-541.
    [10]
    刘新宇,谭力铭,杨春曦,等. 未知环境下的蚁群−聚类自适应动态路径规划[J]. 计算机科学与探索,2019,13(5):846-857. doi: 10.3778/j.issn.1673-9418.1811015

    LIU Xinyu,TAN Liming,YANG Chunxi,et al. Self-adjustable dynamic path planning of unknown environment based on ant colony-clustering algorithm[J]. Journal of Frontiers of Computer Science and Technology,2019,13(5):846-857. doi: 10.3778/j.issn.1673-9418.1811015
    [11]
    敖邦乾,杨莎,叶振环. 改进蚁群算法水面无人艇平滑路径规划[J]. 控制理论与应用,2021,38(7):1006-1014. doi: 10.7641/CTA.2021.00735

    AO Bangqian,YANG Sha,YE Zhenhuan. Improved ant colony algorithm for unmanned surface vehicle smooth path planning[J]. Control Theory & Applications,2021,38(7):1006-1014. doi: 10.7641/CTA.2021.00735
    [12]
    ZHANG Haojian,WANG Yunkuan,ZHENG Jun,et al. Path planning of industrial robot based on improved RRT algorithm in complex environments[J]. IEEE Access,2018,6. DOI: 10.1109/access.2018.2871222.
    [13]
    LI Binghui,CHEN Badong. An adaptive rapidly-exploring random tree[J]. IEEE/CAA Journal of Automatica Sinica,2021,9(2):283-294.
    [14]
    NGUYEN M K,JAILLET L,REDON S. ART-RRT:As-rigid-as-possible exploration of ligand unbinding pathways[J]. Journal of Computational Chemistry Organic Inorganic Physical Biological,2018,39(11):665-678.
    [15]
    BRY A,ROY N. Rapidly-exploring random belief trees for motion planning under uncertainty[C]. IEEE International Conference on Robotics and Automation,Shanghai,2011:723-730.
    [16]
    QURESHI A H,MUMTAZ S,FAHAD L K,et al. Adaptive potential guided directional-RRT[C]. IEEE International Conference on Robotics & Biomimetics,Shenzhen,2014:1887-1892.
    [17]
    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.
    [18]
    司徒华杰,雷海波,庄春刚. 动态环境下基于人工势场引导的RRT路径规划算法[J]. 计算机应用研究,2021,38(3):714-717,724.

    SITU Huajie,LEI Haibo,ZHUANG Chungang. Artificial potential field based RRT algorithm for path planning in dynamic environment[J]. Application Research of Computers,2021,38(3):714-717,724.
    [19]
    李伟东,李乐. 基于改进RRT算法的无人车路径规划[J]. 计算机测量与控制,2023,31(1):160-166.

    LI Weidong,LI Le. Path planning of unmanned vehicle based on improved RRT algorithm[J]. Computer Measurement & Control,2023,31(1):160-166.
    [20]
    陈侠,刘奎武,毛海亮. 基于APF−RRT算法的无人机航迹规划[J]. 电光与控制,2022,29(5):17-22. doi: 10.3969/j.issn.1671-637X.2022.05.004

    CHEN Xia,LIU Kuiwu,MAO Hailiang. UAV path planning based on APF-RRT algorithm[J]. Electronics Optics & Control,2022,29(5):17-22. doi: 10.3969/j.issn.1671-637X.2022.05.004
    [21]
    王道威,朱明富,刘慧. 动态步长的RRT路径规划算法[J]. 计算机技术与发展,2016,26(3):105-107,112.

    WANG Daowei,ZHU Mingfu,LIU Hui. Rapidly-exploring random tree algorithm based on dynamic step[J]. Computer Technology and Development,2016,26(3):105-107,112.
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