Miao Bing, Ge Shirong, Li Yunwang, Guo Yinan
School of Mechanical and Electrical Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China.
Key Laboratory of Intelligent Mining Robotics, Ministry of Emergency Management, Beijing 100083, China.
Sensors (Basel). 2024 Sep 10;24(18):5878. doi: 10.3390/s24185878.
To further enhance the intelligence level of coal mining faces and achieve the autonomous derivation, learning, and optimization of shearer navigation cutting, this paper proposes the methods of shearer digital twin navigation cutting motion planning based on the concept of shearer autonomous navigation cutting technology and intelligent coal mining face digital twins. This study includes the digital twin theory and the construction method of the shearer digital twin navigation cutting motion planning system based on this theory. Based on the digital twin theory, a shearer digital twin navigation cutting motion planning system was constructed. This system supports the service functions of the shearer cutting digital twin, dynamic navigation map digital twin, reinforcement learning environment construction, and motion planning through the physical perception layer, comprehensive data layer, and digital-model fusion analysis layer. Finally, by comparing the effects of the DQN-NAF and DDPG deep reinforcement learning algorithms in the shearer motion planning task within the constructed digital twin environment, the results show that the DQN-NAF algorithm demonstrates better performance and stability in solving the shearer digital twin motion planning task.
为进一步提升采煤工作面智能化水平，实现采煤机导航截割的自主推导、学习与优化，本文基于采煤机自主导航截割技术和智能采煤工作面数字孪生的概念，提出了采煤机数字孪生导航截割运动规划方法。本研究包括数字孪生理论以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。基于数字孪生理论，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、综合数据层和数字模型融合分析层，支持采煤机截割数字孪生、动态导航地图数字孪生、强化学习环境构建以及运动规划等服务功能。最后，通过比较深度Q网络-近邻策略优化算法（DQN-NAF）和深度确定性策略梯度算法（DDPG）在构建的数字孪生环境下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中表现出更好的性能和稳定性。
