Path Planning Strategy of Mobile Nodes Based on Improved RRT Algorithm

Benting Wan, Yu Qin, W. Song
{"title":"Path Planning Strategy of Mobile Nodes Based on Improved RRT Algorithm","authors":"Benting Wan, Yu Qin, W. Song","doi":"10.1109/ICCIA.2018.00051","DOIUrl":null,"url":null,"abstract":"The RRT algorithm is widely used in the high-dimensional path planning in a dynamic environment, and well adapted to the dynamics of motion of the mobile node needs. However, in large scale wireless sensor networks (WSN), the RRT algorithm lacks stability and is easy to deviate from the optimal path. In this paper we proposes a path planning algorithm called E-RRT to improve the problems that RRT has. The method proposed includes the coverage density of obstacle for initialize searching area for the exploring random tree, and the gradually extended region used to ensure the path to be found. The method also adopts the greedy algorithm to delete the intermediate point in the point sequence of path for an optimal path, and the quadratic Bezier curve to smooth the path for the mobile sensor node. The path found can be the shortest, collision-free and smoothing, and therefore to satisfy the requirement of path planning for mobile sensor nodes. The simulation results show that the E-RRT algorithm outperforms the RRT algorithm.","PeriodicalId":297098,"journal":{"name":"2018 3rd International Conference on Computational Intelligence and Applications (ICCIA)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 3rd International Conference on Computational Intelligence and Applications (ICCIA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCIA.2018.00051","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

The RRT algorithm is widely used in the high-dimensional path planning in a dynamic environment, and well adapted to the dynamics of motion of the mobile node needs. However, in large scale wireless sensor networks (WSN), the RRT algorithm lacks stability and is easy to deviate from the optimal path. In this paper we proposes a path planning algorithm called E-RRT to improve the problems that RRT has. The method proposed includes the coverage density of obstacle for initialize searching area for the exploring random tree, and the gradually extended region used to ensure the path to be found. The method also adopts the greedy algorithm to delete the intermediate point in the point sequence of path for an optimal path, and the quadratic Bezier curve to smooth the path for the mobile sensor node. The path found can be the shortest, collision-free and smoothing, and therefore to satisfy the requirement of path planning for mobile sensor nodes. The simulation results show that the E-RRT algorithm outperforms the RRT algorithm.
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
基于改进RRT算法的移动节点路径规划策略
RRT算法广泛应用于动态环境下的高维路径规划,很好地适应了移动节点的动态运动需求。然而,在大规模无线传感器网络中,RRT算法缺乏稳定性,容易偏离最优路径。本文提出了一种称为E-RRT的路径规划算法,以改善RRT存在的问题。该方法采用障碍物覆盖密度作为探索随机树的初始搜索区域,逐步扩展区域用于保证路径被找到。该方法还采用贪心算法删除路径点序列中的中间点以获得最优路径,并采用二次Bezier曲线对移动传感器节点进行路径平滑。找到的路径最短、无碰撞、平滑,从而满足移动传感器节点路径规划的要求。仿真结果表明,E-RRT算法优于RRT算法。
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
Text Extraction and Categorization from Watermark Scientific Document in Bulk Locating Heartbeats from Electrocardiograms and Other Correlated Signals Combining Deep Learning and JSEG Cuda Segmentation Algorithm for Electrical Components Recognition An Oppositional Learning Prediction Operator for Simulated Kalman Filter Clustering Method for Financial Time Series with Co-Movement Relationship
×
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