School of Mechanical and Electrical Engineering, China University of Mining and Technology-Beijing
Institute of Intelligent Mines & Robotics, China University of Mining and Technology-Beijing

为进一步提高采煤工作面的智能化水平，实现采煤机导航截割的自主推演、自主学习和自主优化，基于采煤机自主导航截割技术和数字孪生智采工作面的概念，提出了采煤机数字孪生导航截割运动规划的理论与方法，包括数字孪生理论及基于该理论的采煤机数字孪生导航截割运动规划系统的构建方法。围绕数字孪生理论，探索了智采工作面的物理场景、数字孪生模型的构建及数字孪生驱动、交互和演化机制。为满足不同应用需求，将数字孪生模型分为物理实体、孪生模型和孪生数据模型，详细分析了这3类模型的特点。介绍了由模型驱动、数据驱动和服务驱动组成的3种运行机制，这3种机制通过虚实交互逻辑实现了从感知智能到认知智能的转变。构建了采煤机数字孪生导航截割运动规划系统，该系统通过物理感知层、综合数据层、数据融合分析层及数字孪生服务层，支撑采煤机截割状态数字孪生、动态导航地图数字孪生、数字孪生强化学习环境和强化学习运动规划的服务功能；通过数字化手段将现实中的采煤机导航截割过程复制到数字孪生操作环境中，通过系统内各模块的调用实现数据的自适应融合、智能分析和最优规划。最后，在构建的数字孪生环境中比较深度Q网络−归一化优势函数（DQN−NAF）算法与深度确定性策略梯度（DDPG）算法在采煤机运动规划任务中的效果，结果表明DQN−NAF算法在解决采煤机数字孪生运动规划任务时展现出更优的效果和稳定性。

In order to further improve the intelligence level of coal working faces and achieve autonomous deduction, autonomous learning, and autonomous optimization of shearer navigation cutting, based on the concept of shearer autonomous navigation cutting technology and digital twin smart mining face, the theory and method of shearer digital twin navigation cutting motion planning are proposed. It includes the theory of digital twin and the construction method of shearer digital twin navigation cutting motion planning system based on this theory. Based on the theory of digital twins, the paper explores the physical scenarios of smart mining working face, the construction of digital twin models, and the driving, interactive, and evolutionary mechanisms of digital twins. To meet different application needs, digital twin models are divided into physical entities, twin models, and twin data models. The features of these three types of models are analyzed in detail. Three operational mechanisms, including model driven, data-driven, and service driven, are introduced. The three operational mechanisms achieve the transition from perceptual intelligence to cognitive intelligence through virtual real interaction logic. The study develops a shearer digital twin navigation cutting motion planning system. The system supports the service functions of digital twin cutting status, dynamic navigation map, digital twin reinforcement learning environment, and reinforcement learning motion planning through physical perception layer, comprehensive data layer, data fusion analysis layer, and digital twin service layer. By digital means, the navigation and cutting process of the shearer in reality is replicated in the digital twin operating environment. The adaptive fusion, intelligent analysis, and optimal planning of data are achieved through the calling of various modules within the system. Finally, by comparing the performance of the deep q-network with normalized advantage functions(DQN-NAF) algorithm and the deep deterministic policy gradient (DDPG) algorithm in the motion planning task of shearers in the constructed digital twin environment, the results show that the DQN-NAF algorithm exhibits better performance and stability in solving the digital twin motion planning task of shearers.

国家自然科学基金资助项目（52121003）。

苗丙，葛世荣. 采煤机数字孪生导航截割运动规划理论与方法[J]. 工矿自动化，2024，50（8）：1-13.

MIAO Bing, GE Shirong. Theory and method of shearer digital twin navigation cutting motion planning[J]. Journal of Mine Automation，2024，50（8）：1-13.