arrow
返回

EMI: An Efficient Algorithm for Identifying Maximal Rigid Clusters in 3D Generic Graphs

delete2024-02-01
delete0
PRE
AI
Q
Qinhan Wei
Y
Yongcai Wang *
李德英 封面图
李德英 (Deying Li)
DOI:10.1109/TNET.2023.3287822delete
delete原文链接
delete原文求助
delete分享
delete收藏
摘要

摘要

En 中文
Identifying the Maximal Rigid subGraphs (MRGs) whose relative formations cannot deform continuously in R-d, is a fundamental problem in network formation control and network localization. When d = 3, it becomes extremely challenging and has been open for decades because the fundamental Laman condition doesn't hold in R-3. This paper presents a new understanding of this problem. Because of the existence of implicit hinges in 3D, its essence should be to detect the Maximal Rigid Clusters (MRCs). An MRC is a maximal set of vertices in which each vertex is mutually rigid to the others, but the vertices are not necessarily connected. We show that the MRGs in the original graph can be easily deduced from the connected components generated by the MRCs. For efficiently identifying the MRCs, at first, a randomized algorithm to detect mutually rigid vertex pairs is exploited. Based on this, a Basic MRC Identification algorithm (BMI) is proposed, which is an exact algorithm that can detect all MRCs based on the extracted rigid vertex pairs, but it has O(|V|(4)) time complexity. To further pursue an efficient algorithm, we observe the hinge MRCs appear rarely. So an Efficient framework for MRC Identification (EMI) is proposed. It consists of two steps: 1) a Trimmed-BMI algorithm that guarantees to detect all simple MRCs and may miss only hinge MRCs; 2) a Trim-FIX algorithm that can find all hinge MRCs. We prove EMI can guarantee to detect all the MRCs as accurately as BMI, using O(|V|(3)) times. Further, we show EMI achieves magnitudes of times faster than BMI in experiments. Extensive evaluations verify the effectiveness and high efficiency of EMI in various 3D networks. We have uploaded the code of the related program to https://github.com/fdwqh/EMI-algorithm.
Keyword:
Maximum rigid cluster partition
3D networks
rigid cluster
implicit hinge
mutual rigid pair

期刊

I
IEEE-ACM Transactions on Networking
IF:
3.6
论文数:
4.4K
被引数:
9.5K

机构

R
Renmin University of China
学者数:
8.1K
论文数: 7.7K
被引数: 1.1W
引用论文

引用论文

err分享
err收藏
Distributed Relative Localization Algorithms for Multi-Robot Networks: A Survey
errSENSORS
IF3.5
err2023-02-21
err16
errOAAI
errWang, Shuo; Wang, Yongcai; Li, Deying; Zhao, Qianchuan
err分享
err收藏
err分享
err收藏
学者 查看更多内容