IEEE Robotics Autom. Mag.最新文献_第9页

Delay-compensated event-triggered boundary control of hyperbolic PDEs for deep-sea construction 深海工程双曲偏微分方程的时滞补偿事件触发边界控制

IEEE Robotics Autom. Mag. Pub Date : 2022-04-01 DOI: 10.1016/j.automatica.2021.110137

Ji Wang, M. Krstić

引用次数: 11

Security analysis and defense strategy of distributed filtering under false data injection attacks 虚假数据注入攻击下分布式过滤的安全分析与防御策略

IEEE Robotics Autom. Mag. Pub Date : 2022-04-01 DOI: 10.1016/j.automatica.2021.110151

Jiayu Zhou, Wen Yang, Heng Zhang, W. Zheng, Yong Xu, Yang Tang

引用次数: 14

A new modeling framework for networked discrete-event systems 一种新的网络离散事件系统建模框架

IEEE Robotics Autom. Mag. Pub Date : 2022-04-01 DOI: 10.1016/j.automatica.2021.110139

Ruochen Tai, Liyong Lin, Yuting Zhu, R. Su

引用次数: 9

Scale Fragilities in Localized Consensus Dynamics 局部共识动力学中的尺度脆弱性

IEEE Robotics Autom. Mag. Pub Date : 2022-03-22 DOI: 10.48550/arXiv.2203.11708

E. Tegling, Bassam Bamieh, H. Sandberg

{"title":"Scale Fragilities in Localized Consensus Dynamics","authors":"E. Tegling, Bassam Bamieh, H. Sandberg","doi":"10.48550/arXiv.2203.11708","DOIUrl":"https://doi.org/10.48550/arXiv.2203.11708","url":null,"abstract":"We consider distributed consensus in networks where the agents have integrator dynamics of order two or higher ($nge 2$). We assume all feedback to be localized in the sense that each agent has a bounded number of neighbors and consider a scaling of the network through the addition of agents in a modular manner, i.e., without re-tuning controller gains upon addition. We show that standard consensus algorithms, which rely on relative state feedback, are subject to what we term scale fragilities, meaning that stability is lost as the network scales. For high-order agents ($nge 3$), we prove that no consensus algorithm with fixed gains can achieve consensus in networks of any size. That is, while a given algorithm may allow a small network to converge, it causes instability if the network grows beyond a certain finite size. This holds in families of network graphs whose algebraic connectivity, that is, the smallest non-zero Laplacian eigenvalue, is decreasing towards zero in network size (e.g. all planar graphs). For second-order consensus ($n = 2$) we prove that the same scale fragility applies to directed graphs that have a complex Laplacian eigenvalue approaching the origin (e.g. directed ring graphs). The proofs for both results rely on Routh-Hurwitz criteria for complex-valued polynomials and hold true for general directed network graphs. We survey classes of graphs subject to these scale fragilities, discuss their scaling constants, and finally prove that a sub-linear scaling of nodal neighborhoods can suffice to overcome the issue.","PeriodicalId":13196,"journal":{"name":"IEEE Robotics Autom. Mag.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88465484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Interval Dominance based Structural Results for Markov Decision Process 基于区间优势的马尔可夫决策过程结构结果

IEEE Robotics Autom. Mag. Pub Date : 2022-03-20 DOI: 10.48550/arXiv.2203.10618

V. Krishnamurthy

引用次数: 0

On the sensitivity of pose estimation neural networks: rotation parameterizations, Lipschitz constants, and provable bounds 姿态估计神经网络的敏感性:旋转参数化、Lipschitz常数和可证明界

IEEE Robotics Autom. Mag. Pub Date : 2022-03-16 DOI: 10.48550/arXiv.2203.09937

Trevor Avant, K. Morgansen

引用次数: 0

Distributed Sparse Identification for Stochastic Dynamic Systems under Cooperative Non-Persistent Excitation Condition 协同非持久激励条件下随机动力系统的分布稀疏辨识

IEEE Robotics Autom. Mag. Pub Date : 2022-03-05 DOI: 10.48550/arXiv.2203.02737

Die Gan, Zhixin Liu

{"title":"Distributed Sparse Identification for Stochastic Dynamic Systems under Cooperative Non-Persistent Excitation Condition","authors":"Die Gan, Zhixin Liu","doi":"10.48550/arXiv.2203.02737","DOIUrl":"https://doi.org/10.48550/arXiv.2203.02737","url":null,"abstract":"This paper considers the distributed sparse identification problem over wireless sensor networks such that all sensors cooperatively estimate the unknown sparse parameter vector of stochastic dynamic systems by using the local information from neighbors. A distributed sparse least squares algorithm is proposed by minimizing a local information criterion formulated as a linear combination of accumulative local estimation error and L_1-regularization term. The upper bounds of the estimation error and the regret of the adaptive predictor of the proposed algorithm are presented. Furthermore, by designing a suitable adaptive weighting coefficient based on the local observation data, the set convergence of zero elements with a finite number of observations is obtained under a cooperative non-persistent excitation condition. It is shown that the proposed distributed algorithm can work well in a cooperative way even though none of the individual sensors can fulfill the estimation task. Our theoretical results are obtained without relying on the independency assumptions of regression signals that have been commonly used in the existing literature. Thus, our results are expected to be applied to stochastic feedback systems. Finally, the numerical simulations are provided to demonstrate the effectiveness of our theoretical results.","PeriodicalId":13196,"journal":{"name":"IEEE Robotics Autom. Mag.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84513377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Triggered Gradient Tracking for Asynchronous Distributed Optimization 异步分布式优化的触发梯度跟踪

IEEE Robotics Autom. Mag. Pub Date : 2022-03-04 DOI: 10.48550/arXiv.2203.02210

Guido Carnevale, Ivano Notarnicola, L. Marconi, G. Notarstefano

{"title":"Triggered Gradient Tracking for Asynchronous Distributed Optimization","authors":"Guido Carnevale, Ivano Notarnicola, L. Marconi, G. Notarstefano","doi":"10.48550/arXiv.2203.02210","DOIUrl":"https://doi.org/10.48550/arXiv.2203.02210","url":null,"abstract":"This paper proposes Asynchronous Triggered Gradient Tracking, i.e., a distributed optimization algorithm to solve consensus optimization over networks with asynchronous communication. As a building block, we devise the continuous-time counterpart of the recently proposed (discrete-time) distributed gradient tracking called Continuous Gradient Tracking. By using a Lyapunov approach, we prove exponential stability of the equilibrium corresponding to agents' estimates being consensual to the optimal solution, with arbitrary initialization of the local estimates. Then, we propose two triggered versions of the algorithm. In the first one, the agents continuously integrate their local dynamics and exchange with neighbors their current local variables in a synchronous way. In Asynchronous Triggered Gradient Tracking, we propose a totally asynchronous scheme in which each agent sends to neighbors its current local variables based on a triggering condition that depends on a locally verifiable condition. The triggering protocol preserves the linear convergence of the algorithm and avoids the Zeno behavior, i.e., an infinite number of triggering events over a finite interval of time is excluded. By using the stability analysis of Continuous Gradient Tracking as a preparatory result, we show exponential stability of the equilibrium point holds for both triggered algorithms and any estimate initialization. Finally, the simulations validate the effectiveness of the proposed methods on a data analytics problem, showing also improved performance in terms of inter-agent communication.","PeriodicalId":13196,"journal":{"name":"IEEE Robotics Autom. Mag.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82071373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

IROS 2021 Online in Prague [Society News] IROS 2021在布拉格上线[社会新闻]

IEEE Robotics Autom. Mag. Pub Date : 2022-03-01 DOI: 10.1109/mra.2022.3143189

L. Preucil, Robert Babuška

引用次数: 0

RoboParty: 20 Editions Building Robots and Motivating Youngsters for Science, Technology, Engineering, and Mathematics Subjects [Competitions] robparty: 20个版本，建造机器人，激励青少年学习科学、技术、工程和数学[竞赛]

IEEE Robotics Autom. Mag. Pub Date : 2022-03-01 DOI: 10.1109/mra.2022.3143188

A. Ribeiro, G. Lopes, Nino Pereira, José Cruz

引用次数: 0