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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Jianxin Zeng Yaonan Wang Zhiqiang Miao Wei He Hesheng Wang Department of Electrical and Information Engineering Hunan University Changsha China National Engineering Research Center for Robot Visual Perception and Control Changsha China School of Intelligence Science and Technology University of Science and Technology Beiing Beijing China Key Laboratory of Intelligent Bionic Unmanned Systems Ministry of Education University of Science and Technology Beijing Beijing China Department of Automation Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

For aerial swarms, formation flight has been applied in various scenes. However, most existing works do not consider balancing the conflicting requirements among keeping formation, keeping the smoothness of trajectories, and obstacle avoidance within the limited time. To address this issue, we propose a decentralized trajectory planning framework for formation flight in unknown and dense environments. To ensure that feasible trajectories can be found within the limited time, the formation optimization problem is decoupled into formation affine transformation and iterative trajectory generation. Firstly, the optimization problem based on affine transformation is designed to obtain the optimal affine transformation sequence, which provides the formation reference of trajectory optimization. Secondly, the iterative optimization framework of trajectory planning is designed, which balances the conflicting requirements of formation, smooth flight, and obstacle avoidance. Besides, to escape the local minima caused by non-convex dense environments, the method of topological path planning is designed to provide distinctive initial solutions for trajectory optimization. Finally, the proposed methods are proven to be effective through the simulations and real-world experiments.

关键词： Trajectory planning Planning Iterative methods Collision avoidance Trajectory optimization Intelligent robots

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Consensus Robustness under PI Protocols with Undirected Graphs

Consensus Robustness under PI Protocols with Undirected Grap...

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American control Conference (ACC)

作者： Qi Mao Dan Ma Yanling Ding Hui Peng Jie Chen Department of Electrical Engineering City University of Hong Kong Hong Kong China College of Information Science and Engineering State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China School of Automation Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control Guangdong University of Technology Guangzhou China

This paper concerns robust consensus problems for continuous-time first-order multi-agent systems with respect to uncertain gain and phase of the agent’s frequency response varying within certain ranges. We consider dynamic output feedback control protocol in the form of proportional-integral (PI) control. The agents can in general be nonminimum phase and unstable systems, which are interconnected by an undirected graph network. We seek to determine the largest ranges of gain and phase variations, referred to as the gain consensus margin (GCM) and the phase consensus margin (PCM), respectively, so that consensus can be achieved robustly within these ranges. Our main results consist of explicit analytical expressions of the GCM and PCM, which demonstrate how the agent’s unstable pole and nonminimum phase zero, as well as the network connectivity may fundamentally confine the gain and phase variation ranges so that consensus can or cannot be maintained.

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Robotic Measurement of the Weld Gap Geometry Using a 2D Laser Scanner

Robotic Measurement of the Weld Gap Geometry Using a 2D Lase...

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Mechanical engineering and Robotics Research (ICMERR), International Conference on

作者： Marek Trebul'a Martin Dekan Zuzana Kovaríková Andrej Babinec František Nagy František Duchoň Dušan Labát Institute of Robotics and Cybernetics Faculty of Electrical Engineering and Information Technology STU in Bratislava Bratislava Slovakia Diagnostic and Automation Department VÚEZ a.s. Levice Slovakia

The article describes the importance of robotic measurement of the weld gap geometry using a 2D laser scanner for the needs of a robotic intelligent welding workplace for small-volume production. The article presents algorithms for welding gap detection and the importance of laser-measured data. Furthermore, the article shows the results of measurements in comparison with other available software and the potential use of robotic measurement of the weld gap geometry

关键词： Geometry Welding Laser noise Employment Measurement by laser beam Production Trajectory

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Deep Learning-based Eye-Tracking Analysis for Diagnosis of Alzheimer's Disease Using 3D Comprehensive Visual Stimuli

arXiv

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arXiv 2023年

作者： Zuo, Fangyu Jing, Peiguang Sun, Jinglin Duan, Jizhong Ji, Yong Liu, Yu The School of Microelectronics China School of Electrical and Information Engineering Tianjin University Tianjin China The Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Tianjin Key Laboratory of Cerebrovascular and Neurodegenerative Diseases Department of Neurology Tianjin Dementia Institute Tianjin Huanhu Hospital Tianjin China

Alzheimer's Disease (AD) causes a continuous decline in memory, thinking, and judgment. Traditional diagnoses are usually based on clinical experience, which is limited by some realistic factors. In this paper, we focus on exploiting deep learning techniques to diagnose AD based on eye-tracking behaviors. Visual attention, as typical eye-tracking behavior, is of great clinical value to detect cognitive abnormalities in AD patients. To better analyze the differences in visual attention between AD patients and normals, we first conduct a 3D comprehensive visual task on a non-invasive eye-tracking system to collect visual attention heatmaps. We then propose a multi-layered comparison convolution neural network (MC-CNN) to distinguish the visual attention differences between AD patients and normals. In MC-CNN, the multi-layered representations of heatmaps are obtained by hierarchical convolution to better encode eye-movement behaviors, which are further integrated into a distance vector to benefit the comprehensive visual task. Extensive experimental results on the collected dataset demonstrate that MC-CNN achieves consistent validity in classifying AD patients and normals with eye-tracking data. © 2023, CC BY.

关键词： Eye tracking

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A Machine-learning and Discrete Multi-verse-optimizer-based Hybrid Method for Feature Selection

A Machine-learning and Discrete Multi-verse-optimizer-based ...

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IEEE International Conference on Networking, Sensing and control

作者： Shuo Zhang YingJun Ji XiWang Guo ShuJin Qin Qi Kang Moitrayee Chatterjee Information and Control Engineering Liaoning Petrochemical University Fushun China Faculty of Information Liaoning University Shenyang China Economics and Management Shangqiu Normal University Shangqiu China Electrical and Computer Engineering New Jersey Institute of Technology Newark New Jersey USA Department of Computer Science New Jersey City University New Jersey USA

ISBN: (数字)9798350365221

ISBN: (纸本)9798350365238

Accurately identifying key quality features can significantly streamline the manufacturing process by reducing the number of controlled variables and enhancing product quality prediction. To maximize prediction accuracy while minimizing the number of features, this study proposes a Machine-learning and Discrete Multi-verse-optimizer-based Hybrid method called MDMH. MDMH combines machine learning and the discrete Multi-verse optimizer, utilizing hierarchical clustering for effective feature grouping and employing a wrapper technique to find the optimal solution. After comparing its results with those of the exact solver and other intelligent optimization methods, the proposed method demonstrates superiority in terms of accuracy.

关键词： Accuracy Manufacturing processes Process control Optimization methods Machine learning Feature extraction Product design Quality assessment Sensors Manufacturing

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Universal firefighter sensor for dangerous road tunnel environment

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Transportation Research Procedia 2021年 55卷 1019-1025页

作者： Marek Bujňák Rastislav Pirník Dušan Nemec Marián Hruboš Department of Control and Information Systems Faculty of Electrical Engineering and Information Technology University of Žilina Univerzitná 8215/1 010 26 Žilina Slovakia

This article deals with the issue of detecting and measuring the concentration of dangerous substances in the road tunnel during an accident, in order to minimize the risk to the firefighter during the intervention. The article also includes the design and construction of a mobile sensor system together with the processing of data and a suitable presentation of measured data. The gases are measured by specific detectors of the MQ gas sensors series. NTC type thermistors were used for temperature measuring. The control unit for the mentioned universal sensor is the development board D1 mini Pro, which has an integrated Wi-Fi chip. The universal sensor was designed especially for firefighters intervening during an accident in road tunnels. The measured data are processed and then sent via Wi-Fi network to an external database, from which they are then conveniently presented on the website. The website is then viewed on-line data from sensor for firefighters, whose are intervening during an accident in the tunnel.

关键词： tunnel gas detection dangerous substances MQ sensors universal sensor

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On-line identification methods as part of the tunnel ventilation control algorithm

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Transportation Research Procedia 2021年 55卷 961-968页

作者： Roman Michalík Jozef Hrbček Vojtech Šimák University of Žilina Faculty of Electrical Engineering and Information Technology Department of Control and Information Systems Univerzitná 8215/1 010 26 Žilina Slovakia

The paper presents algorithms of on-line methods of discrete parametric identification of pollutant concentrations in a tunnel tube and comparison of their properties. It describes the characteristics of an individual methods and a detailed evaluation in order to select the most suitable method for the identification of dynamical systems with a significant stochastic component and changing parameters. The aim of the paper is to propose discrete parametric identification methods for systems that have variable parameters. It is a choice of model structure and design of an algorithm that is able to monitor changes in system parameters. The article also describes ways to implement identification algorithms in PLC (Programmable Logic controller) by programming the target device directly from the simulation environment MATLAB Simulink using the toolbox "B&R automation Studio Target for Simulink". From a practical point of view, these algorithms serve to create an apparatus for optimizing the ventilation control in the tunnel and thus ensure the safe passage of vehicles through the tunnel.

关键词： PLC identification algorithm implementation ventilation control in the tunnel

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Two-wheel electric chassis with automatic balancing

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Transportation Research Procedia 2021年 55卷 1026-1033页

作者： Michal Mihálik Marek Bujňák Vojtech Šimák Department of Control and Information Systems Faculty of Electrical Engineering and Information Technology University of Žilina Univerzitná 8215/1 010 26 Žilina Slovakia

This work deals with the construction of an electric vehicle. This vehicle is designed to transport one person for shorter distances in urban areas. The main control member is a microcontroller from Atmel corporation. The aim was to construct vehicle in the best possible ratio of price and quality. The device consists of a mechanical part, a drive part and a control part. The whole vehicle is powered by a battery. A PID controller is used to balance the vehicle.

关键词： Segway microcontroller PID Atmel

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Distributed Event-Triggered Bandit Convex Optimization with Time-Varying Constraints

arXiv

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arXiv 2024年

作者： Zhang, Kunpeng Yi, Xinlei Wen, Guanghui Cao, Ming Johansson, Karl H. Chai, Tianyou Yang, Tao The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China The Laboratory for Information and Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States The Department of Mathematics Southeast University Nanjing210096 China The Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen GroningenAG 9747 Netherlands Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures Stockholm10044 Sweden

This paper considers the distributed bandit convex optimization problem with time-varying inequality constraints over a network of agents, where the goal is to minimize network regret and cumulative constraint violation. Existing distributed online algorithms require that each agent broadcasts its decision to its neighbors at each iteration. To better utilize the limited communication resources, we propose a distributed event-triggered online primal-dual algorithm with two-point bandit feedback. Under several classes of appropriately chosen decreasing parameter sequences and non-increasing event-triggered threshold sequences, we establish dynamic network regret and network cumulative constraint violation bounds. These bounds are comparable to the results achieved by distributed event-triggered online algorithms with full-information feedback. Finally, a numerical example is provided to verify the theoretical results. Copyright © 2024, The Authors. All rights reserved.

关键词： Convex optimization

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Reputation-Based Resilient Consensus with Differential Privacy Guarantees

SSRN

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SSRN 2024年

作者： Ramos, Guilherme Pequito, Sérgio Silvestre, Daniel Departamento de Informática Instituto Superior Técnico Universidade de Lisboa Portugal Instituto de Telecomunicações Lisbon1049-001 Portugal Division of Systems and Control Department of Information Technology Uppsala University Sweden Department of Electrical and Computer Engineering Faculty of Sciences and Technology NOVA University of Lisbon Portugal

Reputation-based consensus methods have gained significant attention in distributed systems to mitigate the impact of malicious agents and ensure reliable decision-making. However, privacy concerns arise when sensitive information is shared among participants. In this paper, we propose a reputation-based consensus method that incorporates resilience and differential privacy guarantees, providing a balance between preserving participants' privacy and maintaining the accuracy of the resilient consensus process. Our approach leverages the notion of differential privacy to add noise to the agents' states, ensuring that individual contributions are masked while still enabling the detection of malicious behavior. We present a detailed analysis of the privacy-accuracy trade-off and demonstrate the effectiveness of our method through simulations. The results show that our reputation-based consensus method with differential privacy guarantees offers robustness against attacks while preserving the privacy of participants, making it a promising solution for privacy-conscious resilient distributed systems. © 2024, The Authors. All rights reserved.

关键词： Decision making

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