Flocking fragmentation formulation for a multi-robot system under multi-hop and lossy ad hoc networks

IF 2.7 3区 工程技术 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Frontiers of Information Technology & Electronic Engineering Pub Date : 2024-08-30 DOI:10.1631/fitee.2300295
Silan Li, Shengyu Zhang, Tao Jiang
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Abstract

We investigate the impact of network topology characteristics on flocking fragmentation for a multi-robot system under a multi-hop and lossy ad hoc network, including the network’s hop count features and information’s successful transmission probability (STP). Specifically, we first propose a distributed communication–calculation–execution protocol to describe the practical interaction and control process in the ad hoc network based multi-robot system, where flocking control is realized by a discrete-time Olfati-Saber model incorporating STP-related variables. Then, we develop a fragmentation prediction model (FPM) to formulate the impact of hop count features on fragmentation for specific flocking scenarios. This model identifies the critical system and network features that are associated with fragmentation. Further considering general flocking scenarios affected by both hop count features and STP, we formulate the flocking fragmentation probability (FFP) by a data fitting model based on the back propagation neural network, whose input is extracted from the FPM. The FFP formulation quantifies the impact of key network topology characteristics on fragmentation phenomena. Simulation results verify the effectiveness and accuracy of the proposed prediction model and FFP formulation, and several guidelines for constructing the multi-robot ad hoc network are concluded.

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多跳和有损特设网络下多机器人系统的成群分片方案
我们研究了多跳、有损特设网络下网络拓扑特征对多机器人系统植群分裂的影响,包括网络的跳数特征和信息的成功传输概率(STP)。具体来说,我们首先提出了一种分布式通信-计算-执行协议,以描述基于 ad hoc 网络的多机器人系统的实际交互和控制过程,其中植群控制是通过一个包含 STP 相关变量的离散时间 Olfati-Saber 模型来实现的。然后,我们开发了一个碎片预测模型(FPM),以制定特定成群情况下跳数特征对碎片的影响。该模型确定了与分片相关的关键系统和网络特征。考虑到受跳数特征和 STP 影响的一般成群情况,我们通过基于反向传播神经网络的数据拟合模型来计算成群分裂概率(FFP),该模型的输入是从 FPM 中提取的。FFP 公式量化了关键网络拓扑特征对分片现象的影响。仿真结果验证了所提出的预测模型和 FFP 公式的有效性和准确性,并总结出构建多机器人特设网络的若干指导原则。
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来源期刊
Frontiers of Information Technology & Electronic Engineering
Frontiers of Information Technology & Electronic Engineering COMPUTER SCIENCE, INFORMATION SYSTEMSCOMPU-COMPUTER SCIENCE, SOFTWARE ENGINEERING
CiteScore
6.00
自引率
10.00%
发文量
1372
期刊介绍: Frontiers of Information Technology & Electronic Engineering (ISSN 2095-9184, monthly), formerly known as Journal of Zhejiang University SCIENCE C (Computers & Electronics) (2010-2014), is an international peer-reviewed journal launched by Chinese Academy of Engineering (CAE) and Zhejiang University, co-published by Springer & Zhejiang University Press. FITEE is aimed to publish the latest implementation of applications, principles, and algorithms in the broad area of Electrical and Electronic Engineering, including but not limited to Computer Science, Information Sciences, Control, Automation, Telecommunications. There are different types of articles for your choice, including research articles, review articles, science letters, perspective, new technical notes and methods, etc.
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