Volume 48 Issue 12
Dec.  2022
Turn off MathJax
Article Contents
Article Navigation >  Journal of Mine Automation  > 2022  >  48(12): 42-48, 56
ZHANG Ye, MA Hongwei, WANG Peng, et al. Research progress and key technologies of intelligent coal-gangue sorting robot[J]. Journal of Mine Automation，2022，48(12)：42-48, 56.  doi: 10.13272/j.issn.1671-251x.2022100048
Citation: ZHANG Ye, MA Hongwei, WANG Peng, et al. Research progress and key technologies of intelligent coal-gangue sorting robot[J]. Journal of Mine Automation，2022，48(12)：42-48, 56.  doi: 10.13272/j.issn.1671-251x.2022100048
shu

Research progress and key technologies of intelligent coal-gangue sorting robot

doi: 10.13272/j.issn.1671-251x.2022100048
  • 1.

    College of Mechanical Engineering, Xi'an University of Science and Technology, Xi 'an 710054, China

  • 2.

    Shaanxi Key Laboratory of Mine Electromechanical Equipment Intelligent Detection and Control, Xi'an 710054, China

  • Received Date: 2022-10-18
  • Rev Recd Date: 2022-11-28
    • Available Online: 2022-11-28
    Abstract
  • The gangue is wrapped by slurry in underground coal mine, which causes difficult coal-gangue recognition and sorting. The underground working space is narrow, so the equipment layout is difficult, and the diversion of coal-gangue is difficult. Therefore, developing a high-performance, highly reliable intelligent coal-gangue sorting robot is necessary. The paper analyzes the research status of coal-gangue recognition, robot trajectory plan and multi-dynamic-target multi-robot collaborative control technology of intelligent coal-gangue sorting robot. This paper points out that the coal-gangue sorting work environment is complex, and its weight and shape of coal-gangue are irregular and randomly distributed. Therefore, the three key technologies for intelligent coal-gangue sorting robot are recognition and grasping features extraction of coal-gangue in complex environment, stable and reliable grasping of coal-gangue in unstructured environment, and intelligent collaborative sorting of multi-target multi-robot. It is proposed that in order to realize the intelligent sorting of coal-gangue by the robot, further research should be carried out. The research includes the methods of coal-gangue recognition and sorting feature extraction suitable for underground, accurate positioning and synchronous tracking of dynamic targets, online trajectory planning of mechanical arms, and intelligent collaborative control of multiple mechanical arms. By soring out the above three key technologies, it can be concluded as follows. The construction and expansion of coal-gangue data set, recognition and grasping features extraction of coal-gangue are the key technologies to achieve efficient coal-gangue recognition. Precise tracking of dynamic coal-gangue, trajectory planning of synchronous tracking dynamic target of mechanical arm and fast and stable grasping of large quality targets are the key technologies to realize stable coal-gangue grasping by mechanical arms. Multi-task efficient allocation, anti-collision path planning and intelligent collaborative control are the key technologies to achieve efficient intelligent collaborative sorting of multiple mechanical arms. According to the common problems at present, this paper puts forward the solutions. In the aspect of recognition, the method of coal-gangue recognition and grasping feature extraction based on multi-mode deep learning is studied to realize fast coal-gangue recognition suitable for the underground. In the aspect of trajectory planning, the precise positioning and real-time tracking methods of dynamic coal-gangue are studied to realize the adaptive and stable grasping of dynamic coal-gangue by the robot. In the aspect of collaborative sorting, a multi-layer multiple mechanical arms collaborative control model is built to achieve efficient intelligent collaborative sorting of multiple mechanical arms in the complex environment.

     

    • FullText(HTML)
  • loading
    • References(36)
  • [1]
    马立强,李永升. 煤矿井下矸石置换煤炭清洁生产技术[J]. 煤炭学报,2010,35(5):816-819.

    MA Liqiang,LI Yongsheng. The clean production technology of replacement of coal gangue in coal mine[J]. Journal of China Coal Society,2010,35(5):816-819.
    [2]
    曹亦俊,刘敏,邢耀文,等. 煤矿井下选煤技术现状和展望[J]. 采矿与安全工程学报,2020,37(1):192-201.

    CAO Yijun,LIU Min,XING Yaowen,et al. Current situation and prospect of underground coal preparation technology[J]. Journal of Mining & Safety Engineering,2020,37(1):192-201.
    [3]
    周少雷,曾志远. 西部煤炭洁净加工利用新技术新工艺探讨[J]. 煤炭加工与综合利用,2016(7):5-10,92.

    ZHOU Shaolei,ZENG Zhiyuan. Inquiry of utilization of new process for clean processing of coal in Western China[J]. Coal Processing & Comprehensive Utilization,2016(7):5-10,92.
    [4]
    李曼,段雍,曹现刚,等. 煤矸分选机器人图像识别方法和系统[J]. 煤炭学报,2020,45(10):3636-3644.

    LI Man,DUAN Yong,CAO Xiangang,et al. Image identification method and system for coal and gangue sorting robot[J]. Journal of China Coal Society,2020,45(10):3636-3644.
    [5]
    王鹏,曹现刚,夏晶,等. 基于机器视觉的多机械臂煤矸石分拣机器人系统研究[J]. 工矿自动化,2019,45(9):47-53.

    WANG Peng,CAO Xiangang,XIA Jing,et al. Research on multi-manipulator coal and gangue sorting robot system based on machine vision[J]. Industry and Mine Automation,2019,45(9):47-53.
    [6]
    董华飞. 基于Delta并联机器人的煤矸石分拣方法研究[D]. 太原: 中北大学, 2022.

    DONG Huafei. Research on coal gangue sorting method based on Delta parallel robot[D]. Taiyuan: North University of China, 2022.
    [7]
    刘鹏,马宏伟,乔心州,等. 柔索驱动拣矸机器人最小索拉力等值曲面研究[J]. 西安科技大学学报,2020,40(5):797-804.

    LIU Peng,MA Hongwei,QIAO Xinzhou,et al. On the contour surfaces of minimum tensions for a cable-driven coal-gangue picking robot[J]. Journal of Xi'an University of Science and Technology,2020,40(5):797-804.
    [8]
    FOOKES R A,GRAVITIS V L,WATT J S,et al. On-line determination of the ash content of coal using a "siroash" gauge based on the transmission of low and high energy γ-rays[J]. International Journal of Applied Radiation & Isotopes,1983,34(1):63-69.
    [9]
    杨慧刚,乔志敏. 基于X射线和机器视觉的煤与矸石分选系统设计[J]. 工矿自动化,2017,43(3):85-89.

    YANG Huigang,QIAO Zhimin. Design of separation system of coal and gangue based on X-ray and machine vision[J]. Industry and Mine Automation,2017,43(3):85-89.
    [10]
    徐琦,孔力,刘文中. 生态遗传算法在煤矸石模糊模式识别中的应用[J]. 工矿自动化,2003,29(2):9-11. doi: 10.3969/j.issn.1671-251X.2003.02.004

    XU Qi,KONG Li,LIU Wenzhong. Application of niche genetic algorithm in fuzzy pattern recognition of gangues[J]. Industry and Mine Automation,2003,29(2):9-11. doi: 10.3969/j.issn.1671-251X.2003.02.004
    [11]
    杨慧刚,乔志敏,高绘彦,等. 煤与矸石分选系统设计[J]. 工矿自动化,2018,44(8):91-95.

    YANG Huigang,QIAO Zhimin,GAO Huiyan,et al. Design of separation system of coal and gangue[J]. Industry and Mine Automation,2018,44(8):91-95.
    [12]
    曹现刚,李莹,王鹏,等. 煤矸石识别方法研究现状与展望[J]. 工矿自动化,2020,46(1):38-43.

    CAO Xiangang,LI Ying,WANG Peng,et al. Research status of coal-gangue identification method and its prospect[J]. Industry and Mine Automation,2020,46(1):38-43.
    [13]
    马宪民. 一种基于小波变换的煤矸石图像边缘检测方法[J]. 仪器仪表学报,2006(增刊3):2130-2131.

    MA Xianmin. Edge detection based on wavelet transform for coal gangue image[J]. Chinese Journal of Scientific Instrument,2006(S3):2130-2131.
    [14]
    MA Xianmin, LIANG Che. Application of rough set theory in coal gangue image process[C]. Proceeding of the Fifth International Conference on Information Assurance and Security, Xi'an, 2009: 87-90.
    [15]
    郜亚松,张步勤,郎利影. 基于深度学习的煤矸石识别技术与实现[J]. 煤炭科学技术,2021,49(12):202-208. doi: 10.3969/j.issn.0253-2336.2021.12.mtkxjs202112025

    GAO Yasong,ZHANG Buqin,LANG Liying. Coal and gangue recognition technology and implementation based on deep learning[J]. Coal Science and Technology,2021,49(12):202-208. doi: 10.3969/j.issn.0253-2336.2021.12.mtkxjs202112025
    [16]
    曹现刚,薛祯也. 基于迁移学习的GoogLenet煤矸石图像识别[J]. 软件导刊,2019,18(12):183-186.

    CAO Xiangang,XUE Zhenye. Coal gangue identification by using transfer learning in GoogLenet[J]. Software Guide,2019,18(12):183-186.
    [17]
    沈科,季亮,张袁浩,等. 基于改进YOLOv5s模型的煤矸目标检测[J]. 工矿自动化,2021,47(11):107-111,118. doi: 10.13272/j.issn.1671-251x.17838

    SHEN Ke,JI Liang,ZHANG Yuanhao,et al. Research on coal and gangue detection algorithm based on improved YOLOv5s model[J]. Industry and Mine Automation,2021,47(11):107-111,118. doi: 10.13272/j.issn.1671-251x.17838
    [18]
    曹珍贯,吕旻姝,张宗唐. 基于热成像技术和深度学习的煤矸石识别方法[J]. 湖南工程学院学报(自然科学版),2021,31(1):48-52. doi: 10.3969/j.issn.1671-119X.2021.01.008

    CAO Zhenguan,LYU Minshu,ZHANG Zongtang. Coal gangue recognition method based on thermal imaging technology and deep learning[J]. Journal of Hunan Institute of Engineering(Natural Science Edition),2021,31(1):48-52. doi: 10.3969/j.issn.1671-119X.2021.01.008
    [19]
    来文豪,周孟然,王锦国,等. 多光谱波段筛选的煤矸石快速定位[J]. 中国激光,2021,48(16):190-200.

    LAI Wenhao,ZHOU Mengran,WANG Jinguo,et al. Fast location of coal gangue based on multispectral band selection[J]. Chinese Journal of Lasers,2021,48(16):190-200.
    [20]
    李亚坤,马宏伟,王鹏. 基于VGG_16网络的煤和矸石识别技术研究[J]. 煤炭技术,2022,41(9):156-159.

    LI Yakun,MA Hongwei,WANG Peng. Research on coal and gangue recognition technology based on VGG_16 network[J]. Coal Technology,2022,41(9):156-159.
    [21]
    赵明辉. 双臂并联煤矸石分拣机器人及其轨迹规划研究[J]. 工矿自动化,2020,46(9):57-63.

    ZHAO Minghui. Research on dual-arm parallel coal gangue sorting robot and its trajectory planning[J]. Industry and Mine Automation,2020,46(9):57-63.
    [22]
    李宁. 煤矸分拣机器人控制系统研究[D]. 西安: 西安科技大学, 2019.

    LI Ning. Study on the control system of coal mine sorting robot[D]. Xi'an: Xi'an University of Science and Technology, 2019.
    [23]
    王鹏,曹现刚,马宏伟,等. 基于余弦定理−PID的煤矸石分拣机器人动态目标稳准抓取算法[J]. 煤炭学报,2020,45(12):4240-4247.

    WANG Peng,CAO Xiangang,MA Hongwei,et al. Dynamic target steady and accurate grasping algorithm of gangue sorting robot based on cosine theorem-PID[J]. Journal of China Coal Society,2020,45(12):4240-4247.
    [24]
    马宏伟,孙那新,张烨,等. 煤矸石分拣机器人动态目标稳定抓取轨迹规划[J]. 工矿自动化,2022,48(4):20-30. doi: 10.13272/j.issn.1671-251x.2021110050

    MA Hongwei,SUN Naxin,ZHANG Ye,et al. Track planning of coal gangue sorting robot for dynamic target stable grasping[J]. Journal of Mine Automation,2022,48(4):20-30. doi: 10.13272/j.issn.1671-251x.2021110050
    [25]
    夏晶,张昊,周世宁,等. 煤矸分拣机器人动态拣取避障路径规划[J]. 煤炭学报,2021,46(增刊1):570-577.

    XIA Jing,ZHANG Hao,ZHOU Shining,et al. Dynamic picking and obstacle avoidance path planning of coal gangue sorting robot[J]. Journal of China Coal Society,2021,46(S1):570-577.
    [26]
    朱子祺,李创业,代伟. 基于G-RRT*算法的煤矸石分拣机器人路径规划[J]. 工矿自动化,2022,48(3):55-62.

    ZHU Ziqi,LI Chuangye,DAI Wei. Path planning of coal gangue sorting robot based on G-RRT* algorithm[J]. Journal of Mine Automation,2022,48(3):55-62.
    [27]
    邹宇星,李立君,高自成. 基于改进PRM的采摘机器人机械臂避障路径规划[J]. 传感器与微系统,2019,38(1):52-56.

    ZOU Yuxing,LI Lijun,GAO Zicheng. Obstacle avoidance path planning for harvesting robot arm based on improved PRM[J]. Transducer and Microsystem Technologies,2019,38(1):52-56.
    [28]
    陈秋莲,蒋环宇,郑以君. 机器人路径规划的快速扩展随机树算法综述[J]. 计算机工程与应用,2019,55(16):10-17. doi: 10.3778/j.issn.1002-8331.1905-0061

    CHEN Qiulian,JIANG Huanyu,ZHENG Yijun. Summary of rapidly-exploring random tree algorithm in robot path planning[J]. Computer Engineering and Applications,2019,55(16):10-17. doi: 10.3778/j.issn.1002-8331.1905-0061
    [29]
    李文彪. 基于深度强化学习的工业机器人避障路径规划方法[J]. 制造业自动化,2022,44(1):127-130. doi: 10.3969/j.issn.1009-0134.2022.01.029

    LI Wenbiao. Obstacle avoidance path planning method for industrial robots based on deep reinforcement learning[J]. Manufacturing Automation,2022,44(1):127-130. doi: 10.3969/j.issn.1009-0134.2022.01.029
    [30]
    商德勇,章林,牛艳奇,等. 煤矸分拣机器人设计与关键技术分析[J]. 煤炭科学技术,2022,50(3):232-238. doi: 10.13199/j.cnki.cst.ZN20-040

    SHANG Deyong,ZHANG Lin,NIU Yanqi,et al. Design and key technology analysis of coal-gangue sorting robot[J]. Coal Science and Technology,2022,50(3):232-238. doi: 10.13199/j.cnki.cst.ZN20-040
    [31]
    WANG Peng, MA Hongwei, ZHANG Ye, et al. A cooperative strategy of multi-arm coal gangue sorting robot based on immune dynamic workspace[J/OL]. International Journal of Coal Preparation and Utilization: 1-21[2022-10-15]. https://doi.org/10.1080/19392699.2022.2078808.
    [32]
    曹现刚,吴旭东,王鹏,等. 面向煤矸分拣机器人的多机械臂协同策略[J]. 煤炭学报,2019,44(增刊2):763-774. doi: 10.13225/j.cnki.jccs.2019.0734

    CAO Xiangang,WU Xudong,WANG Peng,et al. Collaborative strategy of multi-manipulator for coal-gangue sorting robot[J]. Journal of China Coal Society,2019,44(S2):763-774. doi: 10.13225/j.cnki.jccs.2019.0734
    [33]
    黄宛宁,龚建伟,王鹏辉. 基于改进遗传算法的多机器人任务分配方法[J]. 计算机仿真,2006(11):164-167,172. doi: 10.3969/j.issn.1006-9348.2006.11.042

    HUANG Wanning,GONG Jianwei,WANG Penghui. Multi-robot task allocation method based on GA[J]. Computer Simulation,2006(11):164-167,172. doi: 10.3969/j.issn.1006-9348.2006.11.042
    [34]
    李济泽. 基于粒子群遗传优化算法的多机器人任务分配研究[J]. 机械与电子,2007(10):45-48. doi: 10.3969/j.issn.1001-2257.2007.10.016

    LI Jize. Research on mission assign of multi-robot based on particle swarm genetic optimization[J]. Machinery & Electronics,2007(10):45-48. doi: 10.3969/j.issn.1001-2257.2007.10.016
    [35]
    姜健,臧希喆,闫继宏,等. 基于一种蚁群算法的多机器人动态感知任务分配[J]. 机器人,2008(3):254-258,263. doi: 10.3321/j.issn:1002-0446.2008.03.011

    JIANG Jian,ZANG Xizhe,YAN Jihong,et al. Multi-robot dynamically perceived task allocation based on an ant colony algorithm[J]. Robot,2008(3):254-258,263. doi: 10.3321/j.issn:1002-0446.2008.03.011
    [36]
    张子迎,陈云飞,王宇华,等. 基于启发式深度Q学习的多机器人任务分配算法[J]. 哈尔滨工程大学学报,2022,43(6):857-864.

    ZHANG Ziying,CHEN Yunfei,WANG Yuhua,et al. Multi-robot task allocation algorithm based on heuristically accelerated deep Q network[J]. Journal of Harbin Engineering University,2022,43(6):857-864.
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(4)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (392) PDF downloads(76) Cited by()
    Proportional views
    Related

    苏ICP备 05016349号-2

    Supported by: Beijing Renhe Information Technology Co. Ltd.  Visits：    

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return