AGV energy-saving scheduling algorithm for auto parts production workshop based on deep reinforcement learning
Auto parts production workshop has always been one of the key links in the manufacturing industry.In order to adapt to the dynamic and changing logistics handling scene of auto parts production workshop,reduce the distance traveled by AGV(automated guided vehicle)to complete the task and the number of collisions,so as to achieve the purpose of energy saving.An AGV scheduling algorithm based on deep reinforcement learning is proposed.Firstly,the AGV scheduling problem of auto parts production workshop was modeled and the constraint conditions and objective function were defined,and the workshop map was rasterized to build the algorithm training environment.Secondly,the AGV scheduling algorithm based on Double DQN(Deep Q Network)is established,and its state space,action space and reward function are designed.Finally,the experiment proves the energy-saving advantage of AGV scheduling algorithm based on Double DQN compared with traditional scheduling algorithm in dif-ferent problem scenarios.
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