关键词: 深度强化学习, 深度Q网络, 多终点, 自动引导小车, 路径规划

Abstract: The Automatic Guided Vehicle (AGV) undertakes the material transportation between different places in a factory.For the optimal global path,AGV needs to perform path planning with multiple starting points and multiple ending points.The existing deep reinforcement learning algorithms mostly consider the path planning from a single starting point to a single ending point,which have the poor generalization performance for the situation with multiple ending points.Aiming at this problem,a Deep Q-Network (DQN) based solution model for the global optimization of AGV path planning was proposed.The algorithm convergence was promoted by improving the input AGV state and reward function setting;then the starting point position was altered to enhance the data richness and the models perception of the environment,which aimed to improve the models generalization ability of path planning under the environment of different starting points and a single ending point;finally,states of AGVs at different targets were inserted in the training process to achieve the multi-target path planning.Compared with A* and Rapidly-exploring Random Tree (RRT) algorithm under different scale environments and model expansion experiments,the proposed path planning method was verified in the case of multiple targets.

Key words: deep reinforcement learning, deep Q-networks, multi-targets, automatic guided vehicle, path planning