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煤矿掘进多行为协同控制智能决策模型

王宏伟 郄晨飞 付翔 李进 王浩然

王宏伟,郄晨飞,付翔,等. 煤矿掘进多行为协同控制智能决策模型[J]. 工矿自动化,2023,49(6):120-127.  doi: 10.13272/j.issn.1671-251x.18114
引用本文: 王宏伟,郄晨飞,付翔,等. 煤矿掘进多行为协同控制智能决策模型[J]. 工矿自动化,2023,49(6):120-127.  doi: 10.13272/j.issn.1671-251x.18114
WANG Hongwei, QIE Chenfei, FU Xiang, et al. Intelligent decision-making model of multi-behavior collaborative control in coal mine excavation[J]. Journal of Mine Automation,2023,49(6):120-127.  doi: 10.13272/j.issn.1671-251x.18114
Citation: WANG Hongwei, QIE Chenfei, FU Xiang, et al. Intelligent decision-making model of multi-behavior collaborative control in coal mine excavation[J]. Journal of Mine Automation,2023,49(6):120-127.  doi: 10.13272/j.issn.1671-251x.18114

煤矿掘进多行为协同控制智能决策模型

doi: 10.13272/j.issn.1671-251x.18114
基金项目: 国家重点研发计划项目(2020YFB1314004);山西省揭榜招标项目(20201101008);山西省重点研发计划项目(202102100401015)。
详细信息
    作者简介:

    王宏伟(1977—),女,黑龙江勃利人,教授,博士,博士研究生导师,主要研究方向为煤机装备智能化、人工智能与5G+智慧矿山等,E-mail:lntuwhw@126.com

    通讯作者:

    郄晨飞(1997—),男,山西忻州人,硕士研究生,研究方向为煤矿智能化、协同控制,E-mail:1217297735@qq.com

  • 中图分类号: TD632

Intelligent decision-making model of multi-behavior collaborative control in coal mine excavation

  • 摘要: 智能决策支持的掘进多行为协同控制是煤矿掘进工作面智能化的核心之一,掘进多行为协同控制的最优时序规划是智能决策的关键。针对煤矿掘进多行为控制模式单一、固化、协同作业能力差等问题,设计了一种煤矿掘进多行为协同控制智能决策模型,实现了掘进多行为在最优时序下的协同作业。首先,提出了掘进多行为协同控制智能决策方法,确定了掘进多行为可行时序规划集和多目标最优时序规划策略;其次,根据掘进现场的规定和工艺要求,确定了掘进动作事件集,通过对事件集中两两动作事件之间时间关系的分析,求出掘进多行为时间关系约束矩阵;然后,根据时间点关系约束矩阵转换方法,将掘进多行为时间关系约束矩阵转换为时间点关系约束矩阵,再求出掘进多行为可行时序规划集;最后,定义不同掘进目标下的求解函数,求得不同掘进目标的最优时序。实验结果表明,在不同掘进目标下,按照模型决策出的掘进动作最优时序规划结果,掘进机器人可无干涉协同作业,且掘进作业1个工作循环的执行时间与决策模型计算的时间基本一致。

     

  • 图  1  掘进多行为协同控制智能决策方法流程

    Figure  1.  Process of intelligent decision-making method for multi-behavior collaborative control in excavation

    图  2  某回风巷支护工艺

    Figure  2.  Support technology for a return air roadway

    图  3  掘进多行为最优时序计算流程

    Figure  3.  The optimal time series calculation process of multi-behavior in excavation

    图  4  实验设备及部分掘进动作事件标注

    Figure  4.  Experimental equipment and partial excavation action event labeling

    图  5  掘进多行为协同控制实验结果

    Figure  5.  Experimental results of multi-behavior collaborative control in excavation

    表  1  掘进动作事件集

    Table  1.   Excavation action event set

    事件动作类型
    B1掘锚一体机截割煤壁
    B2掘锚一体机左顶钻臂左上顶板第1根钻锚
    B3掘锚一体机右顶钻臂右上顶板第1根钻锚
    B4掘锚一体机左顶钻臂左上顶板第2根钻锚
    B5掘锚一体机右顶钻臂右上顶板第2根钻锚
    B6掘锚一体机左帮钻臂左帮第1根钻锚
    B7掘锚一体机右帮钻臂右帮第1根钻锚
    B8掘锚一体机左帮钻臂左帮第2根钻锚
    B9掘锚一体机右帮钻臂右帮第2根钻锚
    B10锚杆转载机左帮钻臂左帮第3根钻锚
    B11锚杆转载机右帮钻臂右帮第3根钻锚
    B12锚杆转载机左帮钻臂左帮第4根钻锚
    B13锚杆转载机右帮钻臂右帮第4根钻锚
    B14掘锚一体机锚索钻臂顶板第1根锚索钻锚
    B15掘锚一体机锚索钻臂顶板第2根锚索钻锚
    B16掘锚一体机铺设锚网到临时支撑上
    B17掘锚一体机铲板/锚杆转载机前支撑上升
    B18掘锚一体机铲板/锚杆转载机前支撑下降
    B19“两机”后支撑上升
    B20“两机”后支撑下降
    B21掘锚一体机临时支撑升起
    B22“两机”向前行走
    B23掘锚一体机铺设帮锚网
    B24掘锚一体机临时支撑收回
    下载: 导出CSV

    表  2  13种时间元关系表示

    Table  2.   Representations of 13 time element relationships

    两两行为之间13种
    时间关系表示
    符号时间点逻辑法表示图形表示
    Bi
    Bj
    Before<aibiajbj
    Meetmaibi=ajbj
    Overlap0aiajbibj
    Finished Byfiaiajbi =bj
    Containsdiaiajbjbi
    Startsai = ajbibj
    Equal=ai = ajbi =bj
    Start Bysiai = ajbjbi
    Duringdajaibibj
    Finishfajaibi =bj
    Overlaped Byoiajaibjbi
    Meet Bymiajbj =aibi
    After>ajbjaibi
    下载: 导出CSV

    表  3  模型求解目标及对应数学函数

    Table  3.   Model solving objectives and corresponding mathematical functions

    求解目标数学函数
    掘进作业最短时间tmin=min(f(Rk)),f(Rk)为Rk的执行时间
    掘进作业最接近目标时间t={f(Rk)|min(|f(Rk) − S|)},S为目标时间
    掘进作业最长时间tmax=max(f(Rk))
    下载: 导出CSV

    表  4  部分掘进多行为可行时序集

    Table  4.   Feasible timing sets of partial excavation multi-behavior

    序号掘进动作时序
    1a22 [a16 a20 b22] b20 [b16 a21] a18 [a1 a2 a3 a14 a15 b18 b21] [b2 a4] [b3 a5] [b1 a23] b4 b5 [a10 a11] [b10 a12] [b11 a13] b12 b13 a24 b24 [a6 a7 b23] b14 b15 [b6 a8] [b7 a9] b8 b9 [a17 a19] b17 b19
    2a22 [b22 a16] b16 a18 b18 a20 b20 a10 b10 a11 b11 a12 b12 a13 b13 a14 b14 a15 a21 [b21 a1]b1 a2 b2 a3 b3 b15 a4 b4 a5 b5 a23 [b23 a6] b6 a7 b7 a8 b8 a9 b9 a17 b17 a19 b19 a24 b24
    3a22 [a16 a20 b22] b20 [b16 a21] a18 [a1 a2 a3 a14 a15 b18 b21] [b2 a4] [b3 a5] [b1 a23] b4 b5 [a10 a11] [b10 a12] [b11 a13] b12 b13 a24 b24 [a6 a7 b23] b14 b15 [b6 a8] [b7 a9] [b8 b9 a17 a19] b17 b19
    4a22 [a16 a20 b22] b20 [b16 a21] a18 [a1 a2 a3 a14 a15 b18 b21] [b2 a4] [b3 a5] [b1 a23] b4 b5 [a10 a11] [b10 a12] [b11 a13] b12 b13 a24 b24 [a6 a7 b23] b15 b14 [b6 a8] [b7 a9] b9 b8 [a17 a19] b17 b19
    5a22 [a16 a20 b22] b20 [b16 a21] a18 [a1 a2 a3 a14 a15 b18 b21] [b2 a4] [b3 a5] [b1 a23] b4 b5 [a10 a11] [b10 a12] [b11 a13] b12 b13 a24 b24 [a6 a7 b23] b15 b14 [b7 a9] [b6 a8] b9 [b8 a17 a19] b19 b17
    6a22 b22 a16 b16 a18 b18 a20 b20 a10 b10 a11 b11 a12 b12 a13 b13 a14 b14 a15 b15 a21 b21 a1 b1 a2 b2 a3 b3 a4 b4 a5 b5 a23 b23 a6 b6 a7 b7 a8 b8 a9 b9 a17 b17 a19 b19 a24 b24
    7a22 [a16 a20 b22] b20 [b16 a21] a18 [a1 a2 a3 a10 a11 a14 a15 b18] b21 [b2 a4 b3 a5] [b1 b4 b5] [b10 a12 b11] a13 [b12 b13] [ a23 a24] b24 [a6 a7 b23] [b15 b14] [b6 a8 b7 a9] [b9 b8] [ a17 a19] b17 b19
    8a22 [a16 a20 b22] b20 [b16 a21] a18 [a1 a2 a3 a10 a11 a14 a15 b18] b21 [b2 a4 b3 a5] [b1 b4 b5] [b10 a12 b11 a13] [b12 b13] [a23 a24] b24 [a6 a7 b23] [b15 b14] [b6 a8 b7 a9] [b9 b8 a17 a19] [b17 b19]
    下载: 导出CSV

    表  5  掘进动作事件时间

    Table  5.   Excavation action event time

    事件时间/min事件时间/min事件时间/min
    B17.0B93.0B170.3
    B23.0B103.0B180.3
    B33.0B113.0B190.3
    B43.0B123.0B200.3
    B53.0B133.0B210.5
    B63.0B1415.0B221.0
    B73.0B1515.0B231.0
    B83.0B161.0B240.5
    下载: 导出CSV

    表  6  掘进多行为最优时序模型实例化结果

    Table  6.   Instantiation results of optimal time series model for multi-behavior in excavation

    求解目标掘进动作时间点序时间/min
    掘进作业最短时间 a22 [a20 b22 a16 a18][b20 b18 a14 a15 a10 a11][b16 a21] [b21 a1 a2 a3] [b10 b11 a13 a12] [b2 b3 a4 a5] [b12 b13]
    [b4 b5][b1 a23 a24] b24[b23 a6 a7][b6 b7 a8 a9] [b14 b15] [b8 b9 a17 a19] [b19 b17]
    17
    以32 min为掘进作业目标时间 a22 [a20 b22 a16 a18][b20 b18 a10 a11][b16 a21] [b21 a1 a2 a3] [b10 b11 a13 a12] [b2 b3 a4 a5] [b12 b13] [b4 b5]
    [b1 a23 a24] b24[b23 a6 a7][b6 b7 a8 a9] [b8 b9 a14 a15][ b14 b15 a17 a19] [b19 b17]
    32
    掘进作业最长时间 a22 b22 a16 b16 a18 b18 a20 b20 a10 b10 a11 b11 a12 b12 a13 b13 a14 b14 a15 b15 a21 b21 a1 b1 a2 b2 a3 b3 a4 b4 a5 b5
    a23 b23 a6 b6 a7 b7 a8 b8 a9 b9 a17 b17 a19 b19 a24 b24
    79
    下载: 导出CSV
  • [1] 康红普,姜鹏飞,刘畅. 煤巷智能快速掘进技术与装备的发展方向[J]. 采矿与岩层控制工程学报,2023,5(2):5-7.

    KANG Hongpu,JIANG Pengfei,LIU Chang. Development of intelligent rapid excavation technology and equipment for coal mine roadways[J]. Journal of Mining and Strata Control Engineering,2023,5(2):5-7.
    [2] 王国法. 加快煤矿智能化建设 推进煤炭行业高质量发展[J]. 中国煤炭,2021,47(1):2-10. doi: 10.3969/j.issn.1006-530X.2021.01.002

    WANG Guofa. Speeding up intelligent construction of coal mine and promoting high-quality development of coal industry[J]. China Coal,2021,47(1):2-10. doi: 10.3969/j.issn.1006-530X.2021.01.002
    [3] 王步康. 煤矿巷道掘进技术与装备的现状及趋势分析[J]. 煤炭科学技术,2020,48(11):1-11. doi: 10.13199/j.cnki.cst.2020.11.001

    WANG Bukang. Current status and trend analysis of readway driving technology and equipment in coal mine[J]. Coal Science and Technology,2020,48(11):1-11. doi: 10.13199/j.cnki.cst.2020.11.001
    [4] 范京道,魏东,汪青仓,等. 智能化建井理论技术研究与工程实践[J]. 煤炭学报,2023,48(1):470-483.

    FAN Jingdao,WEI Dong,WANG Qingcang,et al. Theory and practice of intelligent coal mine shaft excavation[J]. Journal of China Coal Society,2023,48(1):470-483.
    [5] 郝建生. 煤矿巷道掘进装备关键技术现状和展望[J]. 煤炭科学技术,2014,42(8):69-74. doi: 10.13199/j.cnki.cst.2014.08.018

    HAO Jiansheng. Present status and outlook of key technology for mine roadway heading equipment[J]. Coal Science and Technology,2014,42(8):69-74. doi: 10.13199/j.cnki.cst.2014.08.018
    [6] 杨健健,张强,吴淼,等. 巷道智能化掘进的自主感知及调控技术研究进展[J]. 煤炭学报,2020,45(6):2045-2055.

    YANG Jianjian,ZHANG Qiang,WU Miao,et al. Research progress of autonomous perception and control technology for intelligent heading[J]. Journal of China Coal Society,2020,45(6):2045-2055.
    [7] 马宏伟,王鹏,张旭辉,等. 煤矿巷道智能掘进机器人系统关键技术研究[J]. 西安科技大学学报,2020,40(5):751-759.

    MA Hongwei,WANG Peng,ZHANG Xuhui,et al. Research on key technology of intelligent tunneling robotic system in coal mine[J]. Journal of Xi'an University of Science and Technology,2020,40(5):751-759.
    [8] 吴淼,李瑞,王鹏江,等. 基于数字孪生的综掘巷道并行工艺技术初步研究[J]. 煤炭学报,2020,45(增刊1):506-513. doi: 10.13225/j.cnki.jccs.2019.1453

    WU Miao,LI Rui,WANG Pengjiang,et al. Preliminary study on the parallel technology of fully mechanized roadway based on digital twin[J]. Journal of China Coal Society,2020,45(S1):506-513. doi: 10.13225/j.cnki.jccs.2019.1453
    [9] 付翔,王然风,赵阳升. 液压支架群组跟机推进行为的智能决策模型[J]. 煤炭学报,2020,45(6):2065-2077. doi: 10.13225/j.cnki.jccs.zn20.0339

    FU Xiang,WANG Ranfeng,ZHAO Yangsheng. Intelligent decision-making model on the of hydraulic supports group advancing behavior to follow shearer[J]. Journal of China Coal Society,2020,45(6):2065-2077. doi: 10.13225/j.cnki.jccs.zn20.0339
    [10] 马宏伟,王世斌,毛清华,等. 煤矿巷道智能掘进关键共性技术[J]. 煤炭学报,2021,46(1):310-320. doi: 10.13225/j.cnki.jccs.yg20.1904

    MA Hongwei,WANG Shibin,MAO Qinghua,et al. Key common technology of intelligent heading in coal mine roadway[J]. Journal of China Coal Society,2021,46(1):310-320. doi: 10.13225/j.cnki.jccs.yg20.1904
    [11] 马宏伟,王鹏,王世斌,等. 煤矿掘进机器人系统智能并行协同控制方法[J]. 煤炭学报,2021,46(7):2057-2067. doi: 10.13225/j.cnki.jccs.JJ21.0820

    MA Hongwei,WANG Peng,WANG Shibin,et al. Intelligent parallel cooperative control method of coal mine excavation robot system[J]. Journal of China Coal Society,2021,46(7):2057-2067. doi: 10.13225/j.cnki.jccs.JJ21.0820
    [12] 史忠植,张子云. 基于主体的智能协同决策支持系统[J]. 智能系统学报,2008,3(5):377-383.

    SHI Zhongzhi,ZHANG Ziyun. Agent-based intelligent collaborative decision support system[J]. CAAI Transactions on Intelligent Systems,2008,3(5):377-383.
    [13] 呼守信. 高效快速掘进系统的协同控制[J]. 工矿自动化,2017,43(4):86-88.

    HU Shouxin. Cooperative control of high-efficient and rapid excavation system[J]. Industry and Mine Automation,2017,43(4):86-88.
    [14] COTSAKIS R, ST-ONGE D, BELTRAME G. Decentralized collaborative transport of fabrics using micro-UAVs[C]. International Conference on Robotics and Automation, Montreal, 2018. DOI: 10.1109/ICRA.2019.8793778.
    [15] TAHIR N, PARASURAMAN R. Mobile robot control and autonomy through collaborative simulation twin[EB/OL]. [2023-04-10]. https://arxiv.org/abs/2303.06172.
    [16] 蒋建国,苏兆品,齐美彬,等. 基于强化学习的多任务联盟并行形成策略[J]. 自动化学报,2008,34(3):349-352. doi: 10.3724/SP.J.1004.2008.00349

    JIANG Jianguo,SU Zhaopin,QI Meibin,et al. Multi-task coalition parallel formation strategy based on reinforcement learning[J]. Acta Automatica Sinica,2008,34(3):349-352. doi: 10.3724/SP.J.1004.2008.00349
    [17] 石鹏. 综掘成套装备协同控制研究[D]. 阜新: 辽宁工程技术大学, 2020.

    SHI Peng. Research on cooperative control of comprehensive digging equipment[D]. Fuxin: Liaoning Technical University, 2020.
    [18] 程韬波,李晓晓,徐智浩,等. 基于递归神经网络的多机器人智能协同控制[J]. 机电工程技术,2020,49(5):1-4. doi: 10.3969/j.issn.1009-9492.2020.05.001

    CHENG Taobo,LI Xiaoxiao,XU Zhihao,et al. Intelligent cooperative control of multiple manipulators based on recurrent neural network[J]. Mechanical & Electrical Engineering Technology,2020,49(5):1-4. doi: 10.3969/j.issn.1009-9492.2020.05.001
    [19] 张兴国,张柏,唐玉芝,等. 多机器人系统协同作业策略研究及仿真实现[J]. 机床与液压,2017,45(17):44-51. doi: 10.3969/j.issn.1001-3881.2017.17.011

    ZHANG Xingguo,ZHANG Bai,TANG Yuzhi,et al. Research of the cooperative work strategy in multi-robot system and simulation implement[J]. Machine Tool & Hydraulics,2017,45(17):44-51. doi: 10.3969/j.issn.1001-3881.2017.17.011
    [20] 王国庆,许红盛,王恺睿. 煤矿机器人研究现状与发展趋势[J]. 煤炭科学技术,2014,42(2):73-77.

    WANG Guoqing,XU Hongsheng,WANG Kairui. Research status and development trend of coal mining robots[J]. Coal Science and Technology,2014,42(2):73-77.
    [21] 武星,赵龙,武靖洋,等. 基于改进leader-follower策略的AGV多驱动单元协同控制[J]. 机械设计与制造工程,2018,47(2):35-39. doi: 10.3969/j.issn.2095-509X.2018.02.008

    WU Xing,ZHAO Long,WU Jingyang,et al. Coordinated control of multiple driving units of an automated guided vehicle based on an improved leader-follower strategy[J]. Machine Design and Manufacturing Engineering,2018,47(2):35-39. doi: 10.3969/j.issn.2095-509X.2018.02.008
    [22] 朱雪燕. 矿用皮带机协同控制系统开发[D]. 芜湖: 安徽工程大学, 2018.

    ZHU Xueyan. Mine belt conveyor cooperative control system development[D]. Wuhu: Anhui Polytechnic University, 2018.
    [23] PIERPAOLI P, DOAN T T, ROMBERG J, et al. A reinforcement learning framework for sequencing multi-robot behaviors[EB/OL]. [2023-04-10]. https://arxiv.org/abs/1909.05731v2.
    [24] ZHU Minglei, HUANG Cong, QIU Zhiqiang, et al. Parallel image-based visual servoing/force control of a collaborative delta robot[J]. Frontiers in Neurorobotics, 2022, 16. DOI: 10.3389/fnbot.2022.922704.
    [25] HUANG Zichao,CHU Duanfeng,WU Chaozhong,et al. Path planning and cooperative control for automated vehicle platoon using hybrid automata[J]. IEEE Transactions on Intelligent Transportation Systems,2019,20(3):959-974.
    [26] 李波,刘宾,高明,等. 考虑任务协作的煤矿掘进配套多设备协同控制方法[J]. 机械与电子,2022,40(10):67-71.

    LI Bo,LIU Bin,GAO Ming,et al. Coal mine excavation supporting multi-equipment cooperative control method considering task cooperation[J]. Machinery & Electronics,2022,40(10):67-71.
    [27] 王虹,王步康,张小峰,等. 煤矿智能快掘关键技术与工程实践[J]. 煤炭学报,2021,46(7):2068-2083.

    WANG Hong,WANG Bukang,ZHANG Xiaofeng,et al. Key technology and engineering practice of intelligent rapid heading in coal mine[J]. Journal of China Coal Society,2021,46(7):2068-2083.
    [28] 毛君,董钰峰,卢进南,等. 巷道掘进截割钻进先进技术研究现状及展望[J]. 煤炭学报,2021,46(7):2084-2099. doi: 10.13225/j.cnki.jccs.JJ21.0887

    MAO Jun,DONG Yufeng,LU Jinnan,et al. Research status and prospect of advanced technology of roadway excavation cutting and drilling equipment[J]. Journal of China Coal Society,2021,46(7):2084-2099. doi: 10.13225/j.cnki.jccs.JJ21.0887
    [29] 王虹,王建利,张小峰. 掘锚一体化高效掘进理论与技术[J]. 煤炭学报,2020,45(6):2021-2030.

    WANG Hong,WANG Jianli,ZHANG Xiaofeng. Theory and technology of efficient roadway advance with driving and bolting integration[J]. Journal of China Coal Society,2020,45(6):2021-2030.
    [30] 杨健健,张强,王超,等. 煤矿掘进机的机器人化研究现状与发展[J]. 煤炭学报,2020,45(8):2995-3005. doi: 10.13225/j.cnki.jccs.2019.1452

    YANG Jianjian,ZHANG Qiang,WANG Chao,et al. Status and development of robotization research on roadheader for coal mines[J]. Journal of China Coal Society,2020,45(8):2995-3005. doi: 10.13225/j.cnki.jccs.2019.1452
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出版历程
  • 收稿日期:  2023-05-08
  • 修回日期:  2023-06-15
  • 网络出版日期:  2023-07-10

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