Global path planning of unmanned vehicle based on fusion of A∗ algorithm and Voronoi field

Jiansen Zhao;Xin Ma;Bing Yang;Yanjun Chen;Zhenzhen Zhou;Pangyi Xiao
{"title":"Global path planning of unmanned vehicle based on fusion of A∗ algorithm and Voronoi field","authors":"Jiansen Zhao;Xin Ma;Bing Yang;Yanjun Chen;Zhenzhen Zhou;Pangyi Xiao","doi":"10.1108/JICV-01-2022-0001","DOIUrl":null,"url":null,"abstract":"Purpose - Since many global path planning algorithms cannot achieve the planned path with both safety and economy, this study aims to propose a path planning method for unmanned vehicles with a controllable distance from obstacles. Design/methodology/approach - First, combining satellite image and the Voronoi field algorithm (VFA) generates rasterized environmental information and establishes navigation area boundary. Second, establishing a hazard function associated with navigation area boundary improves the evaluation function of the A∗ algorithm and uses the improved A∗ algorithm for global path planning. Finally, to reduce the number of redundant nodes in the planned path and smooth the path, node optimization and gradient descent method (GDM) are used. Then, a continuous smooth path that meets the actual navigation requirements of unmanned vehicle is obtained. Findings - The simulation experiment proved that the proposed global path planning method can realize the control of the distance between the planned path and the obstacle by setting different navigation area boundaries. The node reduction rate is between 33.52% and 73.15%, and the smoothness meets the navigation requirements. This method is reasonable and effective in the global path planning process of unmanned vehicle and can provide reference to unmanned vehicles' autonomous obstacle avoidance decision-making. Originality/value - This study establishes navigation area boundary for the environment based on the VFA and uses the improved A∗ algorithm to generate a navigation path that takes into account both safety and economy. This study also proposes a method to solve the redundancy of grid environment path nodes and large-angle steering and to smooth the path to improve the applicability of the proposed global path planning method. The proposed global path planning method solves the requirements of path safety and smoothness.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"5 3","pages":"250-259"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9944931/10004521/10004533.pdf","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Intelligent and Connected Vehicles","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10004533/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

Abstract

Purpose - Since many global path planning algorithms cannot achieve the planned path with both safety and economy, this study aims to propose a path planning method for unmanned vehicles with a controllable distance from obstacles. Design/methodology/approach - First, combining satellite image and the Voronoi field algorithm (VFA) generates rasterized environmental information and establishes navigation area boundary. Second, establishing a hazard function associated with navigation area boundary improves the evaluation function of the A∗ algorithm and uses the improved A∗ algorithm for global path planning. Finally, to reduce the number of redundant nodes in the planned path and smooth the path, node optimization and gradient descent method (GDM) are used. Then, a continuous smooth path that meets the actual navigation requirements of unmanned vehicle is obtained. Findings - The simulation experiment proved that the proposed global path planning method can realize the control of the distance between the planned path and the obstacle by setting different navigation area boundaries. The node reduction rate is between 33.52% and 73.15%, and the smoothness meets the navigation requirements. This method is reasonable and effective in the global path planning process of unmanned vehicle and can provide reference to unmanned vehicles' autonomous obstacle avoidance decision-making. Originality/value - This study establishes navigation area boundary for the environment based on the VFA and uses the improved A∗ algorithm to generate a navigation path that takes into account both safety and economy. This study also proposes a method to solve the redundancy of grid environment path nodes and large-angle steering and to smooth the path to improve the applicability of the proposed global path planning method. The proposed global path planning method solves the requirements of path safety and smoothness.
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A*算法和Voronoi场融合的无人车全局路径规划
目的-由于许多全局路径规划算法无法实现既安全又经济的规划路径,本研究旨在为无人车提出一种距离障碍物可控的路径规划方法。设计/方法/方法-首先,将卫星图像和Voronoi场算法(VFA)相结合,生成光栅化的环境信息,并建立导航区域边界。其次,建立与航行区域边界相关的危险函数改进了a*算法的评估函数,并将改进后的a*算法用于全球路径规划。最后,为了减少规划路径中的冗余节点数量并使路径平滑,使用了节点优化和梯度下降法(GDM)。然后,得到满足无人车实际导航要求的连续平滑路径。研究结果-仿真实验证明,所提出的全局路径规划方法可以通过设置不同的导航区域边界来实现对规划路径与障碍物之间距离的控制。节点减少率在33.52%至73.15%之间,平滑度满足导航要求。该方法在无人车全局路径规划过程中合理有效,可为无人车自主避障决策提供参考。独创性/价值-本研究基于VFA为环境建立导航区域边界,并使用改进的A*算法生成兼顾安全和经济的导航路径。本研究还提出了一种解决网格环境路径节点冗余和大角度转向的方法,并对路径进行平滑处理,以提高所提出的全局路径规划方法的适用性。所提出的全局路径规划方法解决了路径安全性和平滑性的要求。
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
CiteScore
7.10
自引率
0.00%
发文量
0
期刊最新文献
Front Cover Contents Advancements and Prospects in Multisensor Fusion for Autonomous Driving Extracting Networkwide Road Segment Location, Direction, and Turning Movement Rules From Global Positioning System Vehicle Trajectory Data for Macrosimulation Decision Making and Control of Autonomous Vehicles Under the Condition of Front Vehicle Sideslip
×
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