智能选矸机器人关键技术研究

Research on key technologies of intelligent gangue sorting robot

  • 摘要: 介绍了智能选矸机器人应用与研究现状,指出目前智能选矸机器人主要基于X射线和图像识别原理,利用高压气动分拣和桁架机器人抓取进行煤矸分离;智能选矸机器人分拣执行机构主要有桁架式、并联式、串联式等类型,响应速度快,常常以“拨”和“抓”的形式分离矸石;在胶带运输过程中,智能选矸机器人“拨”需要考虑不同矸石尺寸的兼容性及运动路径的优化,“抓”需考虑机械手的作业空间及机器人的承载能力。分析了智能选矸机器人在现场复杂环境中有效实现矸石分拣的基于深度学习的煤矸识别、面向非结构多约束环境的选矸机械臂运动规划、基于力反馈的机械臂主动柔顺控制、多臂协作分拣任务分配策略及控制等关键技术,并指出基于深度学习的煤矸识别技术作为选矸机器人的关键技术之一,仍需在煤矸数据集高效构建方法、煤矸识别算法的泛化性提升及实时性优化等方面进行进一步研究。结合现场应用和机器人智能化发展需求,指出了智能选矸机器人今后的研究方向:针对现场复杂环境进行技术改进,提高煤矸识别算法的鲁棒性和自适应性;适应复杂环境的智能感知和控制技术及矸石高精度三维位姿估算技术的研究;基于力位混合控制的选矸机器人智能拣矸技术研发;智能选矸机器人井下选矸技术探究。

     

    Abstract: This paper introduces the application and research status of the intelligent gangue sorting robot. This paper points out that the intelligent gangue sorting robot is mainly based on the principle of X-ray and image identification. And the high-pressure pneumatic sorting and truss robot grasping sorting are used to separate coal and gangue. The sorting actuators are mainly truss type, parallel type and series type of intelligent gangue sorting robot. The sorting actuators have fast response speed and often separate the gangue in the form of 'pulling' and 'grasping'. In the process of belt transportation, the compatibility of different gangue sizes and the optimization of movement path need to be considered in the 'pulling' of the intelligent gangue sorting robot. And the working space of the manipulator and the bearing capacity of the robot need to be considered in the 'grasping'. This paper analyzes the key technologies such as deep learning-based coal and gangue identification, unstructured multi-constraint environment-oriented motion planning of gangue sorting manipulator, force feedback-based active compliance control of manipulator and multi-arm cooperative sorting task allocation strategy and control. These technologies are used for intelligent gangue sorting robot to effectively realize gangue sorting in complex on-site environment. This paper points out that coal and gangue identification technology based on deep learning is one of the key technologies of gangue sorting robot. It still needs further research on the efficient construction method of coal gangue data set, improving the generalization of coal gangue identification algorithm, and the real-time optimization of coal gangue identification algorithm. Combined with the demand of field application and intelligent robot development, the future research directions of intelligent gangue sorting robot are pointed out. In the complex environment on site, it is suggested to improve the robustness and adaptability of the coal gangue identification algorithm. It is suggested to develop intelligent sensing and control technology for complex environment and high-precision three-dimensional pose estimation technology for gangue. It is suggested to develop intelligent gangue picking technology of gangue picking robot based on force position hybrid control. It is suggested to research intelligent gangue sorting robot underground gangue sorting technology.

     

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