基于机器视觉的多机械臂煤矸石分拣机器人系统研究
Research on multi-manipulator coal and gangue sorting robot system based on machine vision
【索引】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qy288千亿国际,2019,45(9):47-53.
【Reference】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.
【DOI】10.13272/j.issn.1671-251x.17442
【作者】 王鹏1,2,曹现刚1,2,夏晶1,2,吴旭东1,2,马宏伟1,2
【Author】 WANG Peng1,2,CAO Xiangang1,2,XIA Jing1,2,WU Xudong1,2,MA Hongwei1,2
【作者机构】1.西安科技大学 机械工程学院, 陕西 西安710054;2.陕西省矿山机电装备智能监测重点实验室, 陕西 西安710054
【Unit】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 Monitoring, Xi'an 710054, China
【摘要】现有煤矸石分拣方法主要是根据煤和岩石的纹理特征值,利用图像处理和模式识别技术对煤矸石进行识别分选,分选的煤矸石粒度为25~150 mm,而对于150 mm以上的煤矸石仍依靠人工进行分选。为了对大粒度煤矸石进行分拣,设计了一种基于机器视觉的多机械臂煤矸石分拣机器人系统。该系统采用机器视觉采集煤矸石信息,应用深度学习方法实现煤矸石识别和抓取特征提取;在获取煤矸石序列信息后,根据煤矸石位置进行排序工作,并通过多目标任务分配策略将抓取任务下达给相应机械臂控制器;机械臂获取任务后,根据获得的任务对目标进行动态监测,当目标进入机械臂工作空间后由视觉伺服系统驱动机械臂完成煤矸石分拣。试验结果表明,该系统可对粒度为50~260 mm的煤矸石进行高效、快速分拣,所采用的煤矸石识别方法和分拣策略在不同带速下具有良好的稳定性和准确性,煤矸识别与定位的综合准确率可达93%,验证了该系统的可行性。
【Abstract】Existing coal and gangue sorting methods mainly use image processing and pattern recognition technology to identify and sort coal and gangue according to texture characteristic values of coal and rock. The grain size of coal and gangue is 25-150 mm, while the coal and gangue above 150 mm still relies on manual sorting. In order to sort coal and gangue with large grain size, a multi-manipulator coal and gangue sorting robot system based on machine vision was proposed.The system uses machine vision to collect coal and gangue information and applies deep learning method to realize coal and gangue identification and grab feature extraction. After obtaining the sequence information of coal and gangue, the sorting work is carried out according to the position of coal and gangue, and the grasping task is assigned to the corresponding manipulator controller by multi-objective task assignment strategy. After the manipulator obtains the task, the target is dynamically monitored according to the acquired task. When the target enters the working space of the manipulator, the visual servo system drives the manipulator to complete the coal and gangue sorting. The test results show that the system can efficiently and quickly sort coal and gangue with grain size of 50-260 mm, and the adopted coal and gangue identification method and sorting strategy have good stability and accuracy under different belt speeds, and the comprehensive accuracy of coal and gangue identification and positioning can reach 93%, which verifies the feasibility of the system.
【关键词】 煤矸石分拣机器人; 大粒度煤矸石; 机械臂; 煤矸石抓取特征; 机器视觉; 视觉伺服; 多机械臂协同控制
【Keywords】coal and gangue sorting robot; coal and gangue with large grain; manipulator; capture characteristics of coal and gangue; machine vision; visual servo; coordinated control with multi-manipulator
【文献出处】工矿qy288千亿国际,2019年9期
【基金】陕西省重点研发计划项目(2018GY-160);陕西省教育厅服务地方科学研究计划项目(18JC022)
【分类号】TD67
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