摘要
为解决无人天车路径规划与智能调度问题,提出了基于深度学习的优化方法。文章设计了智能调度系统架构,采用深度学习技术优化多天车任务的调度决策,以提高系统的调度效率和准确性。在工程实践部分,使用现场部署与联调优化验证了系统在实际应用中的可行性和性能表现,展现了该技术在智能仓储和自动化物流等领域的应用前景。
This paper addresses the issues of unmanned trolley path planning and intelligent scheduling by proposing an optimization method based on deep learning.The study designs an intelligent scheduling system and uses deep learning to optimize the scheduling of multiple trolleys,improving efficiency and accuracy.The engineering practice section validates the system̓s feasibility and performance through field deployment and joint debugging,showing its potential in smart warehousing and automated logistics.
作者
邱福双
QIU Fushuang(Qian̓an Shouxin Automation Information Technology Co.,Ltd.,Tangshan,Hebei 064400,China)
关键词
深度学习
无人天车
路径规划
deep learning
unmanned trolley
path planning
作者简介
邱福双(1987-),本科,工程师,研究方向:计算机高级语言及自动化控制。
