Path Planning Algorithm of Unmanned Vehicle in Mining Area Based on Hierarchical Reinforcement Learning
In complex and dangerous mining environments,path planning for unmanned vehicles in mining areas involves how to enable the vehicle to intelligently choose the best path to achieve safety and efficient operation.However,the traditional path planning algorithm is difficult to effectively deal with the changing road conditions and environment in the mining area.A path planning algorithm for unmanned vehicles in mining area based on hierarchical reinforcement learning is proposed.The al-gorithm trains graph pointer network by hierarchical reinforcement learning technology to solve the path planning problem of unmanned vehicles in mining area.In order to map the vector of the unmanned vehicle nodes in mining area into a low-dimen-sional dense vector,firstly,the context vector of the graph embedding layer is normalized to maintain the global properties of the network.Then,the cross entropy loss function is added to the benchmark function of hierarchical reinforcement learning to measure the difference distribution degree between two different driving vehicles.The experimental results show that this algo-rithm can realize efficient,safe and intelligent path selection in complex mining environment,and the optimization effect of model convergence speed and time cost exceeds the traditional algorithm and professional solver,and has good adaptability and generalization ability.The study results are of great significance for improving the autonomy,efficiency and safety of autonomous driving in mining areas.
mining area driverless vehiclehierarchical reinforcement learningpath planninggraph pointer network
万方数据
维普
