融合多目标与能耗控制的无人仓库内AGV路径规划

A Multi-objective and energy consumption control of AGV routing planning in unmanned warehouse

为降低自动导引车的能耗,以提升其工作效率,促进无人仓库高效运转,提出一种两阶段全局路径规划方法。第一阶段建立了以路径最短与平滑度最大为约束的多目标函数模型,并采用改进后的量子粒子群优化算法进行求解,得出曲率不连续的初始路径;第二阶段根据Bezier曲线与平滑度约束对第一阶段所求的初始路径进行拟合修正,得到速度变化小、能耗低的几何连续路径。仿真结果表明,采用两阶段规划方法得出的路径能够提升自动导引车的工作效率并降低能耗;与常规算法对比,改进算法在优化时间和精度上效果显著。

To reduce the energy consumption,improve the working efficiency of Automated Guided Vehicle(AGV)and promote the high-efficiency operation of the unmanned warehouses,a two-stage global routing planning method was proposed.In the primary stage,a multi-objective function model targeting to acquire the shortest and smoothest routing was established.By adopting the improved quantum particle swarm optimization algorithm,a feasible path which conformed to the constraint and features discontinuous curvature was obtained.In the second stage,according to the Bezier curve and the smoothness constraint,the initial path fitting correction obtained in the first stage was obtained,and a geometric continuous path with small speed variation and low energy consumption was obtained.The simulation results showed that the routing presented by two-stage planning method could improve the efficiency of AGV and decrease the energy consumption.While compared with the conventional algorithm,the improved algorithm had a significant improvement in terms of time and accuracy.