Theory and method of shearer digital twin navigation cutting motion planning

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.