基于CA-RL的堆石坝填筑仓面碾压作业动态路径规划OA北大核心CSTPCD

Rolling operation is essential to the construction of rockfill dam.Scientific and reasonable planning of the rolling path of warehouse surface can improve the rolling efficiency and maintain the rolling quality.Current re-search on roller path planning during storehouse surfaces compaction lacks in-depth consideration of dynamic factors such as changes in the number of rolling machines and real-time analysis of compaction quality.Regarding this situation,this paper proposes a dynamic path planning method for rolling machine groups based on reinforce-ment learning-instructed cellular automata model instructed.First,a cellular automata-based rolling surface infor-mation model is established,and a method for evaluating the overall compaction quality of strips is proposed to store and update the compaction quality and other warehouse surface information.Then,a path planning model based on reinforcement learning is established,the state set and action set are constructed,the reward function is designed and the utilization strategy is explored to solve the path assignment problem as the number of rolling ma-chines changes.Coupled with the above two models,the dynamic path planning of roller groups in rockfill dam construction is realized.The engineering application shows that the proposed method can dynamically consider changing factors such as number of rollers and perception of compaction quality.The planned path reduces in length by 22.3％on average compared with on-site construction while maintaining high compaction quality.The proposed method can significantly improve the rolling efficiency.