定制家具板材搬运AGV路径规划

目的 针对应用在定制家具生产包装车间的板材搬运自动引导车存在的路径规划时间长以及规划出的路径转弯次数较多的问题,提出一种改进Q-learning算法的路径规划算法.方法 根据定制家具生产包装车间环境情况,使用栅格法对车间进行建模,建立车间模型的人工标量场,使用人工标量场给予Q-learning算法前期搜索的目的性;增加Q-learning算法的学习层,使得算法可以更快进入收敛;在奖励函数中加入转弯惩罚,使得算法规划出的路径具有更少的转弯次数.结果 仿真结果表明改进后的算法与标准Q-learning算法、增加学习层的Q-learning算法、引入人工势能场Q-learning算法、深度双Q网络算法相比,迭代次数减少了70.46％,64.40％,67.75％,30.49％,转弯次数减少了80％,80.95％,83.33％,73.33％.结论 板材搬运自动引导车利用改进后的Q-learning算法可以有效地提高路径的平滑性,降低了路径规划的时间,提高了板材搬运自动引导车的工作效率.

AGV Path Planning of Customized Furniture Plate Transportation

Aiming at the problems of long path planning time and more turns in the planned path of automatic guided vehicle used in customized furniture production and packaging workshop, a path planning algorithm based on improved Q-learning algorithm is proposed. According to the environment of customized furniture production and packaging workshop, the grid method was used to model the workshop. The artificial scalar field of the workshop environment was established to make purpose of the early search of the learning layer Q-learning algorithm more clear, and the turning penalty was added to the reward function, so as to quickly plan a short path with less turns. Simulation results show that the iterations of the improved algorithm reduced by 70.46%, 64.40%, 67.75%, and 30.49%, respectively compared with standard Q-learning algorithm, learning layer Q-learning, artificial potential field Q-learning, and deep double Q network algorithm. Turning times reduced by 80%, 80.95%, 83.33%, 73.33% respectively. The improved Q-learning algorithm can effectively improve the smoothness of the path, reduce the time of path planning, and improve the efficiency of the automatic guided vehicle.