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Multi-View Landmark-Assisted UAV Swarm 6-DoF Pose Estimation Using Resonant Beam and VIO

IEEE Internet of Things Journal 2025年

作者： Du, Siyuan Xu, Mengyuan Li, Yixuan Fang, Wen Liu, Qingwen Yi, Peng Hong, Yiguang Tongji University School of Computer Science and Technology Shanghai201804 China Tongji University College of Electronic and Information Engineering Shanghai201804 China Chinese Academy of Sciences Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing100190 China

As an essential aerial platform in Internet of Things (IoT) applications, UAV swarms require high-precision attitude estimation in GPS-limited and dynamic environments, which supports higher-level IoT functions such as smart logistics, disaster response, and environmental monitoring. However, most odometry-based pose estimation methods in dynamic scenarios without GPS encounter issues with cumulative errors over time. In this paper, we propose a method to reduce these cumulative errors by constraining the absolute positioning of Visual-Inertial Odometry (VIO) using relative poses obtained from multi-view landmark and Resonant-Beam (RBeam) sensors between UAVs. For synchronous moments, we estimate the 6 Degree-of-Freedom (DoF) relative pose using the Angle of Arrival and Time of Flight data from the RBeam, combined with nonlinear optimization. For asynchronous moments, a two-stage visual estimation method is introduced, combining multi-camera epipolar geometry for rotation recovery and depth reconstruction optimization for translation recovery, enabling the estimation of asynchronous relative poses. Finally, we design a global objective function based on a sliding window and factor graph, integrating RBeam, multi-view landmark, and VIO for absolute 6-DoF pose optimization of the UAV swarm. Simulation results demonstrate that optimizing with the addition of RBeam synchronous relative poses improves overall positioning accuracy by 32.94% compared to pure VIO. Incorporating both RBeam synchronous and visual asynchronous relative poses further enhances overall positioning accuracy by 37.65%. Additionally, the UAV’s attitude benefits from the rotational constraints provided by the RBeam, achieving over 30% improvement in the pitch and yaw directions. © 2014 IEEE.

关键词： Global positioning system

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A novel LMI-based method for robust stabilization of fractional-order interval systems with 1 ≤ α < 2

arXiv

引用

arXiv 2020年

作者： Badri, Pouya Sojoodi, Mahdi Advanced Control Systems Laboratory School of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran

This paper deals with the problem of robust dynamic output feedback stabilization of interval fractional-order linear time invariant (FO-LTI) systems with the fractional order 1 ≤ α Copyright © 2020, The Author... 详细信息

关键词： Linear matrix inequalities

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SAMOC: Enabling Atomic Invocations for Cross-chain Crowdsourcing Testing DApps in Industrial control Through Trusted Smart Community and Lock Mechanism

SAMOC: Enabling Atomic Invocations for Cross-chain Crowdsour...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Weiguo Huang Yong Ding Jun Li Yujue Wang Hai Liang Changsong Yang Guangxi Key Laboratory of Cryptography and Information Security School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Guangxi Engineering Research Center of Industrial Internet Security and Blockchain Guilin University of Electronic Technology Guilin China Lion Rock Labs of Cyberspace Security Institute of Cyberspace Technology HKCT Institute for Higher Education China China Industrial Control Systems Cyber Emergency Response Team Beijing China Hangzhou Innovation Institute of Beihang University Hangzhou China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

Crowdsourcing testing leverages extensive user participation to identify and fix potential issues in industrial control software, ensuring its security and reliability. Decentralized applications (DApps) can utilize blockchain and smart contract technologies to ensure the decentralization, transparency, and tamper-resistance of industrial control software testing. However, testing efforts conducted via DApps are typically confined to a single blockchain. Coordinating and scheduling DApps deployed on different blockchains has become a critical challenge. Moreover, DApps need to verify the credibility of crowdsourcing participants to ensure the reliability of testing. In order to address these issues, this paper proposes SAMOC, an atomic cross-chain system based on the Atomic-Oracle chain, which aims to realize atomic cross-chain invocations for DApps. SAMOC can realize atomic cross-chain invokes, and the intelligent community deployed on the Atomic-Oracle chain calculates the reputation of users’ accounts across multiple chains through Oracle. This paper also designs a corresponding incentive mechanism to ensure that the auditing of cross-chain invokes is honest and trustworthy. Experimental results show that the cross-chain invokes of the proposed SAMOC scheme have lower latency compared to AtomCI.

关键词： Crowdsourcing Software testing Smart cities Industrial control Smart contracts Decentralized applications Software Blockchains Software reliability Security

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SAMOC: Enabling Atomic Invocations for Cross-chain Crowdsourcing Testing DApps in Industrial control Through Trusted Smart Community and Lock Mechanism 23

SAMOC: Enabling Atomic Invocations for Cross-chain Crowdsour...

引用

23rd IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2024

作者： Huang, Weiguo Ding, Yong Li, Jun Wang, Yujue Liang, Hai Yang, Changsong Guilin University of Electronic Technology Guangxi Key Laboratory of Cryptography and Information Security School of Computer Science and Information Security Guilin China Hkct Institute for Higher Education Lion Rock Labs of Cyberspace Security Institute of Cyberspace Technology 999077 China Guilin University of Electronic Technology Guangxi Engineering Research Center of Industrial Internet Security and Blockchain Guilin China China Industrial Control Systems Cyber Emergency Response Team Beijing China Hangzhou Innovation Institute of Beihang University Hangzhou China

Crowdsourcing testing leverages extensive user participation to identify and fix potential issues in industrial control software, ensuring its security and reliability. Decentralized applications (DApps) can utilize blockchain and smart contract technologies to ensure the decentralization, transparency, and tamper-resistance of industrial control software testing. However, testing efforts conducted via DApps are typically confined to a single blockchain. Coordinating and scheduling DApps deployed on different blockchains has become a critical challenge. Moreover, DApps need to verify the credibility of crowdsourcing participants to ensure the reliability of testing. In order to address these issues, this paper proposes SAMOC, an atomic cross-chain system based on the Atomic-Oracle chain, which aims to realize atomic cross-chain invocations for DApps. SAMOC can realize atomic cross-chain invokes, and the intelligent community deployed on the Atomic-Oracle chain calculates the reputation of users' accounts across multiple chains through Oracle. This paper also designs a corresponding incentive mechanism to ensure that the auditing of cross-chain invokes is honest and trustworthy. Experimental results show that the cross-chain invokes of the proposed SAMOC scheme have lower latency compared to AtomCI. © 2024 IEEE.

关键词： Software testing

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Multi-Loss Function for Distance-to-collision Estimation

Multi-Loss Function for Distance-to-collision Estimation

引用

International Conference on Informative and Cybernetics for Computational Social systems (ICCSS)

作者： Xiangzhu Zhang Lijia Zhang Ding Xu Hailong Pei Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Unmanned Aerial Vehicle Systems Engineering Technology Research Center of Guangdong South China University of Technology Guangzhou China School of Computer Science and Technology Guangdong University of Technology Guangzhou China

ISBN: (纸本)9781665402460

Estimation of the drone’s distance-to-collision is the key to the indoor autonomous obstacle avoidance and navigation of monocular UAVs. At present, the distance-to-collision model mainly uses regression loss or ordinal regression for training Regression loss utilizes the continuity of distance, and ordinal regression loss utilizes the order of distance. To improve the prediction performance of the model, this paper proposes a multi-loss function trained deep learning model based on the linear combination of ordinal regression loss and regression loss. The regression loss can be obtained by adding a distance decoder after the ordinal regression estimation, without changing the original structure of the model. Finally, we test the model performance in public datasets and obtain good results.

关键词： Training Deep learning Navigation Estimation Predictive models Decoding Collision avoidance

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A survey on security and privacy issues of UAVs

arXiv

引用

arXiv 2021年

作者： Mekdad, Yassine Aris, Ahmet Babun, Leonardo Fergougui, Abdeslam E.L. Conti, Mauro Lazzeretti, Riccardo Uluagac, A. Selcuk Laboratory of Computer Networks and Systems Moulay Ismail University of Meknes Zitoune Meknes11201 Morocco Cyber-Physical Systems Security Lab Department of Electrical and Computer Engineering Florida International University MiamiFL33174 United States Department of Mathematics University of Padua Padua35121 Italy Department of Computer Control and Management Engineering "Antonio Ruberti" Sapienza University of Rome Rome00185 Italy

In the 21st century, the industry of drones, also known as Unmanned Aerial Vehicles (UAVs), has witnessed a rapid increase with its large number of airspace users. The tremendous benefits of this technology in civilian applications such as hostage rescue and parcel delivery will integrate smart cities in the future. Nowadays, the affordability of commercial drones expands its usage at a large scale. However, the development of drone technology is associated with vulnerabilities and threats due to the lack of efficient security implementations. Moreover, the complexity of UAVs in software and hardware triggers potential security and privacy issues. Thus, posing significant challenges for the industry, academia, and governments. In this paper, we extensively survey the security and privacy issues of UAVs by providing a systematic classification at four levels: Hardware-level, Software-level, Communication-level, and Sensor-level. In particular, for each level, we thoroughly investigate (1) common vulnerabilities affecting UAVs for potential attacks from malicious actors, (2) existing threats that are jeopardizing the civilian application of UAVs, (3) active and passive attacks performed by the adversaries to compromise the security and privacy of UAVs, (4) possible countermeasures and mitigation techniques to protect UAVs from such malicious activities. In addition, we summarize the takeaways that highlight lessons learned about UAVs’ security and privacy issues. Finally, we conclude our survey by presenting the critical pitfalls and suggesting promising future research directions for security and privacy of UAVs. © 2021, CC BY.

关键词： Drones

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Generalization Analysis of Machine Learning Algorithms via the Worst-Case Data-Generating Probability Measure

arXiv

引用

arXiv 2023年

作者： Zou, Xinying Perlaza, Samir M. Esnaola, Iñaki Altman, Eitan INRIA Centre Inria d'Université Côte d'Azur Sophia Antipolis06902 France Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States GAATI Laboratory Université de la Polynésie Française Faaa98702 French Polynesia Department of Automatic Control and Systems Engineering University of Sheffield SheffieldS1 3JD United Kingdom Laboratoire d'Informatique d'Avignon Université d'Avignon France

In this paper, the worst-case probability measure over the data is introduced as a tool for characterizing the generalization capabilities of machine learning algorithms. More specifically, the worst-case probability measure is a Gibbs probability measure and the unique solution to the maximization of the expected loss under a relative entropy constraint with respect to a reference probability measure. Fundamental generalization metrics, such as the sensitivity of the expected loss, the sensitivity of the empirical risk, and the generalization gap are shown to have closed-form expressions involving the worst-case data-generating probability measure. Existing results for the Gibbs algorithm, such as characterizing the generalization gap as a sum of mutual information and lautum information, up to a constant factor, are recovered. A novel parallel is established between the worst-case data-generating probability measure and the Gibbs algorithm. Specifically, the Gibbs probability measure is identified as a fundamental commonality of the model space and the data space for machine learning algorithms. © 2023, CC BY.

关键词： Learning algorithms

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Lyapunov characterizations of input-to-state stability for discrete-time switched systems via finite-step Lyapunov functions

引用

IFAC-PapersOnLine 2020年第2期53卷 2016-2021页

作者： Maryam Sharifi Navid Noroozi Rolf Findeisen School of Electrical and Computer Engineering University of Tehran Tehran Iran Laboratory for Systems Theory and Automatic Control Otto-von-Guericke University Magdeburg Germany

This paper addresses Lyapunov characterizations of input-to-state stability for nonlinear switched discrete-time systems via finite-step Lyapunov functions with respect to closed sets. The use of finite-step Lyapunov functions permits not-necessarily input-to-state stable systems in the systems family, while input-to-state stability of the resulting switched system is ensured. The result is generally presented for systems under arbitrary switching. It additionally covers the case of constrained switchings. We illustrate the effectiveness of our results by application to networked control systems with periodic scheduling policies under a priori known and dwell time-based switching mechanism.

关键词： Discrete-time switched systems Lyapunov methods input-to-state stability finite-step Lyapunov functions networked control systems

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Twisting Sliding Mode control of an Electrostatic MEMS Micromirror for a Laser Scanning System

引用

IEEE/CAA Journal of Automatica Sinica 2019年第4期6卷 1060-1067页

作者： Hui Chen Zhendong Sun Weijie Sun John Tze Wei Yeow the School of Computer and Information Engineering Henan UniversityKaifeng 475000China the Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190 China the College of Automation Science and Engineering South China University of TechnologyGuangzhou 510641China the Systems Design Engineering University of WaterlooWaterloo N2L3G1Canada IEEE

In this paper,we present a twisting control scheme with proportional-integral-derivative(PID)sliding surface for a two-axis electrostatic torsional micromirror,and the utilization of the proposed scheme in a laser scanning *** experimental results of set-point regulation verify that the proposed scheme provides enhanced transient response and positioning performance as compared to traditional sliding mode *** evaluate the tracking performance of the closed-loop system,triangular waves with different frequencies are used as desired *** the proposed scheme the experimental results verified that the closed-loop controlled micromirror follows the given triangular trajectories precisely.A micromirror-based laser scanning system is developed to obtain *** compared with open-loop control,the experimental results demonstrated that the proposed scheme is able to reduce the distortion of the raster scan,and improve the imaging performance in the presence of cross-coupling effect.

关键词： Electrostatic micromirror imaging microelectromechanical system(MEMS) twisting algorithm

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control of Smart Buildings and Utility-Scale Batteries Enabling 24/7 Carbon-Free Energy*

Control of Smart Buildings and Utility-Scale Batteries Enabl...

引用

European control Conference (ECC)

作者： Faran A. Qureshi Jeff S. Shamma Robotics Intelligent Systems and Control (RISC) Laboratory Computer Electrical and Mathematical Sciences and Engineering (CEMSE) Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia University of Illinois at Urbana-Champaign USA

Wind power is a clean and sustainable source of electricity, but its intermittent nature creates challenges for its grid integration. Utility-scale batteries are a popular and effective solution to make wind power dispatchable, however, batteries are expensive and increase the overall cost of operation. In this paper, we propose utilizing the flexibility in the consumption of smart buildings to reduce the size of the utility-scale batteries needed for the dispatchability of wind energy. A method based on stochastic optimization is proposed to compute the dispatch plan required to participate in the day-ahead energy market. A stochastic model predictive controller is proposed for the control of the buildings and batteries to track the dispatch plan in real-time. Simulations are carried out with realistic building models and real weather, wind power production, and forecast data. Results demonstrate that utilizing the flexibility of building thermodynamics can significantly reduce the size (and usage) of the required battery for making the wind power production dispatchable.

关键词： Smart buildings Computational modeling Stochastic processes Production Wind power generation Predictive models Data models

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