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CLC number: TP393

On-line Access: 2024-08-27

Received: 2023-10-17

Revision Accepted: 2024-05-08

Crosschecked: 2023-10-18

Cited: 0

Clicked: 974

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Tao JIANG

https://orcid.org/0000-0002-1388-7478

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Frontiers of Information Technology & Electronic Engineering  2024 Vol.25 No.8 P.1057-1076

http://doi.org/10.1631/FITEE.2300295


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


Author(s):  Silan LI, Shengyu ZHANG, Tao JIANG

Affiliation(s):  The Research Center of 6G Mobile Communications, School of Cyber Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China; more

Corresponding email(s):   lisilan@hust.edu.cn, shengyu@hust.edu.cn, taojiang@hust.edu.cn

Key Words:  Multi-robot flocking, Flocking fragmentation probability, Fragmentation prediction, Multi-robot communication networks


Silan LI, Shengyu ZHANG, Tao JIANG. Flocking fragmentation formulation for a multi-robot system under multi-hop and lossy ad hoc networks[J]. Frontiers of Information Technology & Electronic Engineering, 2024, 25(8): 1057-1076.

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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.

多跳有损自组网下多机器人集群分裂模型构建

李思岚1,2,张绳昱1,2,江涛1
1华中科技大学网络空间安全学院6G移动通信研究中心,中国武汉市,430074
2华中科技大学武汉光电国家研究中心,中国武汉市,430074
摘要:在多机器人集群系统中,不可靠的通信网络可能引发群体分裂现象,进而为集群任务带来不利影响。本文研究网络拓扑特征参数对集群分裂现象的影响,以期为多机器人集群系统的网络构建提供理论指导。具体地,首先针对多机器人集群系统提出一种分布式"通信-计算-执行"协议,以表征多跳有损自组织网络下机器人的信息交互和运动控制过程。该协议考虑了信息的单跳及多跳传输成功概率,并利用离散时间Olfati-Saber模型实现集群控制。基于该协议,针对特定初始状态下的集群场景构建了分裂预测模型。该模型明确了与集群分裂现象相关的关键系统状态特征及网络拓扑特征,可在确定性网络拓扑下根据系统初始状态完成群体分裂预测。根据这些特征,进一步利用基于反向传播神经网络的数据拟合方法,构建了集群分裂概率模型,可表征网络拓扑参数与集群分裂概率之间的函数关系。仿真结果验证了所提预测模型和集群分裂概率模型的有效性和准确性。最后,对多机器人集群自组网的构建提出指导建议。

关键词:多机器人集群;集群分裂概率;分裂预测;多机器人通信网络

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