A Reinforcement Learning-Based Crushing Method for Robots Operating Within Smart Fully Mechanized Mining Faces

The current method of manually handling or using open-loop automation to deal with abnormal coal lumps on the scraper conveyor is inefficient due to constraints, such as safety concerns and equipment wear. To address inefficiencies in the handling of abnormal coal blocks on scraper conveyors, a reinforcement-learning-based method is proposed. Aiming to address the issue that experimenting on abnormal coal handling by scraper conveyors is expensive, this paper designs a variational Auto-Encoder model with the U-MLP network as its core to simulate the processing environment. In addition, given the sparse characteristics of coal block point cloud data, a deep reinforcement learning model based on the LKDG model is designed to control the crushing equipment when dealing with abnormal coal blocks. Through the point cloud data, images, and other information collected by the fully mechanized mining laboratory before and after abnormal processing of coal blocks, we built a simulation environment for abnormal coal blocks, and trained the LKDG model in the simulation environment. To validate the proposed model, we compared LKDG with baseline models in simulation experiments. The results demonstrate that this method can effectively enhance the efficiency of abnormal coal lump processing without human intervention: LKDG achieved a 10.92% higher average reward compared to existing approaches. In terms of engineering applicability, the trained LKDG delivered excellent performance in laboratory tests conducted in a fully mechanized mining environment, increasing the effective crushing count by 67.11% over conventional automated processing methods.