Diffusion-Induced Barrier Coverage of Multi-Layer Robotic Sensing Networks With Adaptive Scheduling Strategy

IF 3.2 3区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS
Pengyang Fan, Chao Zhai, Hehong Zhang
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引用次数: 0

Abstract

The timely identification of external intruders is crucial to the protection of concerned regions or targets against malicious attacks. Multi-agent barrier coverage provides a powerful framework for the effective deployment of sensor networks to monitor baleful intruders. This paper aims to address the barrier coverage problem of robotic sensing networks by developing a multi-layer coverage formulation. Inspired by gas diffusion, a group of robotic sensors are scattered to expand the coverage territory from a gathering spot. With the assistance of a divide-and-conquer scheme, a distributed control algorithm is proposed to partition the defence barrier into multiple curve segments and integrate it with intruder monitoring. By adaptively scheduling robotic sensors among multi-layer networks, it contributes to maximizing the joint detection probability of sensing networks against external intruders. Moreover, theoretical analysis is conducted to acquire sufficient conditions for elevating the detection quality of a multi-layer sensing network. Finally, numerical simulations and robotic experiments are carried out to demonstrate the effectiveness of the proposed barrier coverage approach.

基于自适应调度策略的多层机器人传感网络扩散诱导屏障覆盖
及时识别外部入侵者对于保护相关区域或目标免受恶意攻击至关重要。多智能体屏障覆盖为有效部署传感器网络监控恶意入侵者提供了一个强大的框架。本文旨在通过开发一种多层覆盖公式来解决机器人传感网络的屏障覆盖问题。受气体扩散的启发,一组机器人传感器被分散开来,从一个聚集点扩展覆盖范围。利用分而治之的方法,提出了一种分布式控制算法,将防御屏障划分为多个曲线段,并与入侵者监控相结合。通过在多层网络中自适应调度机器人传感器,使传感网络对外部入侵的联合检测概率最大化。并进行了理论分析,获得了提高多层传感网络检测质量的充分条件。最后,通过数值模拟和机器人实验验证了所提出的屏障覆盖方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
International Journal of Robust and Nonlinear Control
International Journal of Robust and Nonlinear Control 工程技术-工程:电子与电气
CiteScore
6.70
自引率
20.50%
发文量
505
审稿时长
2.7 months
期刊介绍: Papers that do not include an element of robust or nonlinear control and estimation theory will not be considered by the journal, and all papers will be expected to include significant novel content. The focus of the journal is on model based control design approaches rather than heuristic or rule based methods. Papers on neural networks will have to be of exceptional novelty to be considered for the journal.
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