• 论文
主办单位:煤炭科学研究总院有限公司、中国煤炭学会学术期刊工作委员会
基于多机械臂协同的煤矸分拣方法研究
  • Title

    Study on coal-gangue sorting method based on multi-manipulator collaboration

  • 作者

    曹现刚费佳浩王鹏李宁苏玲玲

  • Author

    CAO Xiangang,FEI Jiahao,WANG Peng,LI Ning ,SU Lingling

  • 单位

    西安科技大学机械工程学院

  • Organization
    School of Mechanical Engineering,Xi’an University of Science and Technology,Xi’an ,China
  • 摘要

    为了实现煤和矸石的自动化和智能化分选,降低煤炭洗选的投资和生产成本,提高煤炭质量,针对国内外目前煤炭分选方式存在的资源浪费、环境污染,分选效率较低和成本较高等问题,提出了一种多动态目标多机械臂协同的煤矸分拣方案,依据国内煤矿的煤矸分选现状和迭煤厂的工况,设计了一种适应于狭小工作空间和速度较快手拣矸输送带的煤矸分拣机器人,该机器人构建了一种基于深度学习的煤矸快速识别方法,通过实时采集煤和矸石流的图像数据,利用训练好的卷积神经网络模型自动提取煤矸图像特征,给出准确的识别信息,并通过双目立体视觉技术对已识别的矸石进行三维信息提取。由于机器人分拣目标为动态目标,对分拣运动进行了规划,采用三自由度桁架式机械臂进行分拣,提出多机械臂协同的煤矸分拣策略。试验结果表明 :基于多机械臂协同的煤矸分拣机器人能够有效改善煤矸分选方法,对煤矸混合物进行快速识别和定位,煤矸混合样本下的目标识别准确率可达76.92%;采用多臂协同方案能够提升煤矸分拣效率,最终实现对煤矸快速分拣。

  • Abstract
    Aiming at the current mainstream coal and gangue sorting method, there was problems such as large waste of resources, heavy environmental pollution and low sorting efficiency. A multi - manipulator collaboration coal and gangue sorting robot was proposed, and the multi-dynamic target and multi-manipulator cooperation method of the robot was studied. In order to solve the problem of gangue identification in industrial environment, a rapid identification method based on deep learning network was proposed, which realized real-time identification of coal gangue flow on picking tape machine, and effectively improves its comprehensive accuracy. The relative coordinates and depth information of gangue were obtained in real time by binocular vision technology, And the corresponding error analysis and error compensation methods of 3D information were studied, which provide a basis for the coal and gangue sorting of the robot. In order to achieve efficient sorting of multiple gangue in the sorting area, a multi-dynamic target,multi- manipulator coordinated coal,gangue sorting strategy and corresponding sorting process were proposed. The self-planning test results of the manipulator picking trajectory were realized. The rapid identification and positioning method of the target recognition accuracy rate under the mixed sample reached 76.92%. The multi-arm collaborative sorting method significantly improved the efficiency of coal gangue sorting compared with the single-arm method. In summary, the coal-gangue sorting robot based on multi- manipulator collaboration provided a feasible method for realizing the automatic and intelligent sorting of coal and gangue, reducing the investment and production cost of coal washing and improving coal quality.
  • 关键词

    煤矸分选深度学习煤矸识别机器人

  • KeyWords

    coal-gangue sorting robot;deep learning; coal-gangue identification;collaborative strategy

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主办单位:煤炭科学研究总院有限公司 中国煤炭学会学术期刊工作委员会

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