摘要
针对当前智能仓库中多仓储机器人协同规划效率低下、动态性不足等问题,提出了一种融合改进蛇优化(Improved Snake Optimizer,ISO)算法和时间窗(Time Window,TW)模型的路径规划方法.首先,在静态规划阶段,利用考虑阻塞因素的改进蛇优化算法为多仓储机器人规划出全局最优路径,同时计算出各机器人的时间窗,从而提升仓储机器人的规划效率;其次,在动态规划阶段,通过建立的多仓储机器人时间窗模型并引入动态调节策略,以消解冲突提升算法的动态性能;最后,进行仿真及实验.仿真结果表明,ISO-TW算法相较其他算法在阻塞点数量上最多可减少27～43个,减少幅度达22.50％～31.62％(机器人规模为80个);重规划路径比重可降低39.82％～41.05％(机器人规模为40个);平均运行时长最多可缩短38～40 s,缩短幅度约为18.27％～19.05％(机器人规模为60个).实验结果表明,ISO-TW算法较其他算法平均运行时长减少8.53％～9.23％,冲突次数减少11.63％～15.56％,能够在真实场景中实现多仓储机器人的高效协同规划.
Abstract
To solve the low efficiency and insufficient dynamics of cooperative planning of multi-warehouse robots in current intel-ligent warehouses,a path planning method combining Improved Snake Optimizer(ISO)and Time Window(TW)model was pro-posed.Firstly,in the static planning stage,the improved snake optimizer considering the blocking factor was used to plan the glob-al optimal path for multi-warehouse robots,and the time window of each robot was calculated,so as to improve the planning effi-ciency of warehouse robots.Secondly,in the dynamic planning stage,the time window model of multi-warehouse robots was estab-lished and dynamic adjustment strategy was introduced to resolve conflicts and improve the dynamic performance of the algorithm.Finally,simulation and experiments were conducted,the simulation results showed that the ISO-TW algorithm could re-duce the number of blocking points by up to 27～43 compared to other algorithms with a reduction of 22.50％～31.62％(with a robot size of 80).The proportion of replanned paths could be reduced by 39.82％～41.05％(with a robot scale of 40).The average running time could be shortened by up to 38～40 s with a reduction of approximately 18.27％～19.05％(with a scale of 60 robots).Experimental results showed that the ISO-TW algorithm reduces the average running time by 8.53％～9.23％and the number of conflicts by 11.63％～15.56％compared to other algorithms.It could achieve efficient collaborative planning of multi-warehouse robots in real scenarios.
基金项目
江苏省高等学校自然科学研究项目(21KJB580005)
维普
万方数据