云环境下的仓储物流机器人路径规划方法研究

Research on path planning method of warehouse logistics robot in cloud environment

针对现有仓储物流机器人路径规划方法存在的规划路径长、规划效率低等问题,基于云计算网络的仓储机器人路径规划系统,采用全局路径规划与局部路径规划相结合的方法,提出了一种基于云计算平台的运算处理方法,将改进A*算法和改进人工势场法相结合用于仓储机器人的路径规划。通过蚁群算法对A*算法进行优化,完成全局路径规划。通过修正势场函数和加入逃逸因子优化人工势场法,完成局部路径调整。通过试验验证了所提方法的准确性和优越性。结果表明,所提混合规划方法在规划路径和时间消耗方面均优于改进前,通过云平台提高了算法的执行效率。

In response to the problems of long planning paths and low planning efficiency in existing path planning methods for warehousing and logistics robots,a warehousing robot path planning system based on cloud computing networks adopts a combination of global path planning and local path planning.The algorithm is proposed based on a cloud computing platform,which combines improved A*algorithm and improved artificial field method for path planning of warehousing robots,optimizes the A*algorithm through ant colony algorithm to complete global path planning.By modifying the potential field function and adding escape factors to optimize the artificial potential field method,local path adjustment is completed.The experiments verifies accuracy and superiority of this method.The results show that the proposed hybrid planning method outperforms the previous one in terms of path planning and time consumption,and improves the execution efficiency of the algorithm through cloud platforms.