摘要
提出了一种基于改进后的两阶段控制策略和多目标的带约束遗传算法的控制策略,并用通过速度调节的冲突解决模式,实施对AGV系统的优化调度。首先利用遗传算法离线生成k条最优路径,再采用速度调节的冲突解决模式对其进行在线动态路径规划;若k条路径均不能满足条件,则用带约束多目标遗传算法计算最优路径。仿真证明:本文提出的调度策略,大大增加了AGV调度系统的柔性、效率和鲁棒性,提高了系统效率,为实际应用提供了技术依据。
A systematic control strategy is presented for scheduling the multiple AGV system. Such a strategy is based on an improved two-stage traffic control scheme, muti-objective genetic algorithm with constraints and conflict avoidance policy by regulating speed. First, k candidate paths are prepared by genetic algorithm off-line, and stored in the form of routing table. Then the on-line traffic controller utilizes the table to generate a collision-free path by the conflict avoidance policy ; if the k candidate paths are not suitable, then a suitable path is to be generated by genetic algorithm with constraints. It is proved with simulation that the strategy improves flexibility, robustness and efficiency of the AGV system.
出处
《机械科学与技术》
CSCD
北大核心
2008年第9期1211-1216,共6页
Mechanical Science and Technology for Aerospace Engineering
基金
江苏省精密与微细制造技术重点实验室基金项目(JSPM200701)
江苏省物流自动化装备工程技术研究中心基金项目(BM2006806)资助
关键词
AGV
调度系统
两阶段控制策略
带约束的遗传算法
动态路径规划
automated guided vehicle（AGV）
two-stage control scheme
genetic algorithm with constraints
dynamic path planning
