Space-time Map based Path Planning Scheme in Large-scale Intelligent Warehouse System

Xiao Fu, Changle Li, Yilong Hui, Jie Yang, Wuchao Pei, Su Wang
{"title":"Space-time Map based Path Planning Scheme in Large-scale Intelligent Warehouse System","authors":"Xiao Fu, Changle Li, Yilong Hui, Jie Yang, Wuchao Pei, Su Wang","doi":"10.1109/ITSC45102.2020.9294691","DOIUrl":null,"url":null,"abstract":"As an important part of large-scale intelligent warehouse system, path planning by considering the cooperation among automated guided vehicles (AGVs) becomes an important factor to enhance the efficiency of the system. To this end, we propose a novel path planning scheme based on space-time map with the target of improving the path planning efficiency. Specifically, we first model the time dimension and construct a space-time map to obtain the planned path information of the intelligent warehouse system. Then, by taking the size of AGV and turning cost into consideration, we design a node extension algorithm to limit the search direction of AGVs. To decrease the complexity of the proposed algorithm and improve the efficiency of head-on conflict avoidance, a time window based piecewise path planning method and a mechanism of protected zone are developed, respectively. Simulation results show that the proposed space-time map based path planning scheme has a better performance than the conventional method in terms of the number of turns, the system running time and the moving distance of AGVs.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ITSC45102.2020.9294691","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

As an important part of large-scale intelligent warehouse system, path planning by considering the cooperation among automated guided vehicles (AGVs) becomes an important factor to enhance the efficiency of the system. To this end, we propose a novel path planning scheme based on space-time map with the target of improving the path planning efficiency. Specifically, we first model the time dimension and construct a space-time map to obtain the planned path information of the intelligent warehouse system. Then, by taking the size of AGV and turning cost into consideration, we design a node extension algorithm to limit the search direction of AGVs. To decrease the complexity of the proposed algorithm and improve the efficiency of head-on conflict avoidance, a time window based piecewise path planning method and a mechanism of protected zone are developed, respectively. Simulation results show that the proposed space-time map based path planning scheme has a better performance than the conventional method in terms of the number of turns, the system running time and the moving distance of AGVs.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于时空地图的大型智能仓库系统路径规划方案
作为大型智能仓库系统的重要组成部分,考虑自动导引车(agv)之间协作的路径规划成为提高系统效率的重要因素。为此,以提高路径规划效率为目标,提出了一种基于时空映射的路径规划方案。具体来说,我们首先对智能仓库系统的时间维度进行建模,并构造时空映射来获得智能仓库系统的规划路径信息。然后,考虑到AGV的大小和成本,设计节点扩展算法来限制AGV的搜索方向。为了降低算法的复杂度和提高正面冲突避免的效率,分别提出了基于时间窗的分段路径规划方法和保护区机制。仿真结果表明,本文提出的基于时空地图的路径规划方案在转弯数、系统运行时间和agv移动距离等方面都优于传统的路径规划方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
CR-TMS: Connected Vehicles enabled Road Traffic Congestion Mitigation System using Virtual Road Capacity Inflation A novel concept for validation of pre-crash perception sensor information using contact sensor Space-time Map based Path Planning Scheme in Large-scale Intelligent Warehouse System Weakly-supervised Road Condition Classification Using Automatically Generated Labels Studying the Impact of Public Transport on Disaster Evacuation
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1