Coal Geology & Exploration


In order to improve the support efficiency of coal mine roadway and the automation intelligence of heading face, an anchor drilling robot integrating the cantilever roadheader and the six-degree-of-freedom manipulator was designed, and studied on the anchor drilling sequence planning strategy and the drilling boom trajectory planning method. Firstly, the kinematics model of the manipulator structure was established based on the improved DH method. On this basis, the forward and inverse kinematics solutions were analyzed, and the spatial position and the corresponding joint variable values of the drilling rig were calculated. Secondly, the manipulator space was analyzed based on Monte Carlo random number method, and the motion range of dual manipulators was solved. Then, the drilling sequence planning strategy was put forward based on the technical requirements of drilling and anchorage, so that the two manipulators could complete the roof anchoring task according to a specific anchoring order under different conditions. Finally, the trajectory planning of the manipulator was completed based on the quintic polynomial interpolation method, so that the drill boom could reach the target position quickly and smoothly. The simulation results show that the anchor drilling robot can meet the requirements of roadway support in the range of 6 000 mm wide × 4 500 mm high. Based on the hole sequence planning strategy and the drilling boom trajectory planning method, the two rigs can effectively avoid the interference in the roof anchorage operation, realize the accurate and smooth movement to the anchorage point and complete the anchorage task. The research results provide a way of thinking and method for the development of automatic support and intelligent mining.


anchor drilling robot, kinematic model, workspace, trajectory planning, quintic polynomial interpolation, coal mine


[1] 王国法,张建中,薛国华,等. 煤矿回采工作面智能地质保障技术进展与思考[J]. 煤田地质与勘探,2023,51(2):12−26.

WANG Guofa, ZHANG Jianzhong, XUE Guohua, et al. Progress and reflection of intelligent geological guarantee technology in coal mining face[J]. Coal Geology & amp; Exploration,2023,51(2):12−26.

[2] 朱伟,王虹,李首滨,等. 煤矿采掘装备核心控制技术现状和发展趋势[J]. 煤炭科学技术,2020,48(12):153−160.

ZHU Wei,WANG Hong,LI Shoubin,et al. Current status and development trend of core control technology for coal mining and tunneling equipment[J]. Coal Science and Technology,2020,48(12):153−160.

[3] 康红普. 我国煤矿巷道锚杆支护技术发展60年及展望[J]. 中国矿业大学学报,2016,45(6):1071−1081.

KANG Hongpu. Sixty years development and prospects of rock bolting technology for underground coal mine roadways in China[J]. Journal of China University of Mining and Technology,2016,45(6):1071−1081.

[4] 王国法,任怀伟,赵国瑞,等. 煤矿智能化十大“痛点”解析及对策[J]. 工矿自动化,2021,47(6):1−11.

WANG Guofa,REN Huaiwei,ZHAO Guorui,et al. Analysis and countermeasures of ten’ pain points’ of intelligent coal mine[J]. Industry and Mine Automation,2021,47(6):1−11.

[5] 张旭辉,杨文娟,薛旭升,等. 煤矿远程智能掘进面临的挑战与研究进展[J]. 煤炭学报,2022,47(1):579−597.

ZHANG Xuhui,YANG Wenjuan,XUE Xusheng,et al. Challenges and developing of the intelligent remote control on roadheaders in coal mine[J]. Journal of China Coal Society,2022,47(1):579−597.

[6] 韩玉辉. 液压凿岩台车自动定位钻孔关键技术研究[D]. 徐州:中国矿业大学,2019.

HAN Yuhui. Research on key technologies of automatic location drilling for rock drilling jumbo[D]. Xuzhou:China University of Mining and Technology,2019.

[7] 肖永前,郭勇,周烜亦,等. 三臂凿岩机器人孔序规划及其优化[J]. 传感器与微系统,2019,38(4):73−75.

XIAO Yongqian,GUO Yong,ZHOU Xuanyi,et al. Sequence planning of three–boom tunnel–drilling robot and optimizing[J]. Transducer and Microsystem Technologies,2019,38(4):73−75.

[8] 王恒升,陈伟锋,王思远. 基于末端杆件螺旋运动的凿岩机械臂孔序规划[J]. 中国机械工程,2016,27(13):1748−1754.

WANG Hengsheng,CHEN Weifeng,WANG Siyuan. Drilling sequence planning based on screw motion of end lever of a tunneling rig manipulator[J]. China Mechanical Engineering,2016,27(13):1748−1754.

[9] CHEN Fei,SEKIYAMA K,CANNELLA F,et al. Optimal subtask allocation for human and robot collaboration within hybrid assembly system[J]. IEEE Transactions on Automation Science and Engineering,2014,11(4):1065−1075.

[10] JOSE K,PRATIHAR D K. Task allocation and collision–free path planning of centralized multi–robots system for industrial plant inspection using heuristic methods[J]. Robotics and Autonomous Systems,2016,80:34−42.

[11] 吴万荣,殷建坤. 一种三臂凿岩台车孔序规划[J]. 合肥工业大学学报 (自然科学版),2013,36(2):149−151.

WU Wanrong,YIN Jiankun. Bore sequence planning of a three–arm rock–drilling rig[J]. Journal of Hefei University of Technology (Natural Science Edition),2013,36(2):149−151.

[12] 魏鹏,罗红波,赵康,等. 基于蚁群算法的运动时间优化算法研究[J]. 四川大学学报 (自然科学版),2018,55(6):1171−1179.

WEI Peng,LUO Hongbo,ZHAO Kang,et al. Optimization of multi–joint robot motion of hydraulic drilling vehicle based on ant colony algorithm[J]. Journal of Sichuan University (Natural Science Edition),2018,55(6):1171−1179.

[13] MA Hongwei,WEI Xiaorong,WANG Peng,et al. Multi–arm global cooperative coal gangue sorting method based on improved Hungarian algorithm[J]. Sensors,2022,22(20):7987.

[14] 张伟民,张月,张辉. 基于改进 A*算法的煤矿救援机器人路径规划[J]. 煤田地质与勘探,2022,50(12):185−193.

ZHANG Weimin,ZHANG Yue,ZHANG Hui. Path planning of coal mine rescue robot based on improved A* algorithm[J]. Coal Geology & Exploration,2022,50(12):185−193.

[15] 郝雪弟,景新平,张中平,等. 机器人化钻锚车钻臂工作空间分析及轨迹规划[J]. 中南大学学报(自然科学版),2019,50(9):2128−2137.

HAO Xuedi,JING Xinping,ZHANG Zhongping,et al. Workspace analysis and trajectory planning of manipulator of roboticized bolting truck[J]. Journal of Central South University (Science and Technology),2019,50(9):2128−2137.

[16] 赵业和,刘达新,刘振宇,等. 基于多种群竞争松鼠搜索算法的机械臂时间最优轨迹规划[J]. 浙江大学学报(工学版),2022,56(12):2321−2329.

ZHAO Yehe,LIU Daxin,LIU Zhenyu,et al. Time–optimal trajectory planning of manipulator based on multi–group competition squirrel search algorithm[J]. Journal of Zhejiang University (Engineering Science),2022,56(12):2321−2329.

[17] 石宪闪,苗鸿宾,张伟. 基于改进粒子群算法的六自由度机械臂时间最优轨迹规划[J]. 机床与液压,2023,51(1):20−25.

SHI Xianshan,MIAO Hongbin,ZHANG Wei. Time optimal trajectory planning of 6−DOF manipulator based on improved particle swarm optimization algorithm[J]. Machine Tool & Hydraulics,2023,51(1):20−25.

[18] 韩涛,李静,黄友锐,等. 煤矿救援机器人机械臂轨迹规划算法研究[J]. 工矿自动化,2021,47(11):45−52.

HAN Tao,LI Jing,HUANG Yourui,et al. Research on trajectory planning algorithm of manipulator arm of coal mine rescue robot[J]. Journal of Mine Automation,2021,47(11):45−52.

[19] 赵晶,祝锡晶,孟小玲,等. 改进鲸鱼优化算法在机械臂时间最优轨迹规划的应用[J]. 机械科学与技术,2023,42(3):388−395.

ZHAO Jing,ZHU Xijing,MENG Xiaoling,et al. Application of improved whale optimization algorithm in time–optimal trajectory planning of manipulator[J]. Mechanical Science and Technology for Aerospace Engineering,2023,42(3):388−395.

[20] 熊俊涛,李中行,陈淑绵,等. 基于深度强化学习的虚拟机器人采摘路径避障规划[J]. 农业机械学报,2020,51(增刊2):1−10.

XIONG Juntao,LI Zhonghang,CHEN Shumian,et al. Obstacle avoidance planning of virtual robot picking path based on deep reinforcement learning[J]. Transactions of the Chinese Society of Agricultural Machinery,2020,51(Sup.2):1−10.

[21] KOFINAS N,ORFANOUDAKIS E,LAGOUDAKIS M G. Complete analytical forward and inverse kinematics for the NAO humanoid robot[J]. Journal of Intelligent & Robotic Systems,2015,77(2):251−264.

[22] 殷建坤. 多臂凿岩台车孔序规划研究[D]. 长沙:中南大学,2013.

YIN Jiankun. Research on bore sequence planning of more booms rock drilling machine[D]. Changsha:Central South University,2013.



To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.