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Automated Drone Charging Station Using Gravity and Shape of Landing Structure

Journal of Institute of Control, robotics and Systems 2025年第2期31卷 146-152页

作者： Woo Lee, Jae Jeong, Seongkyun Hwan Bong, Jae Department of Human Intelligent Robot Engineering Sangmyung University Korea Republic of

This paper proposes a novel automated charging station for small drones. This system uses the three-dimensional shape of the landing structure and gravity to guide small drones, which are challenging to control precise landings, to the charging position. It employs a contact-based charging method using a single actuator to drive the vertical one-degree-of-freedom motion of the charging cable, enabling connection to the drone's charging terminal. The developed system continuously verifies the start of charging and tries to connect the charging cable to the drone until it successfully initiates charging. Performance evaluations demonstrate high charging success rates across various situations, with drones landing on the station at different positions and orientations. Most experiments have achieved successful charging within five attempts to connect the charging cable to the drone. This research demonstrates that mechanical design, utilizing the landing structure's shape and gravity, can achieve stable drone charging automation. The developed system is simply designed, making it cost-effective and easy to maintain. © ICROS 2025.

关键词： automated charging drone charging station gravity-based alignment landing structure design

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Fractional Order Iteration for Gradient Descent Method Based on Event-Triggered Mechanism

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Journal of Systems Science & Complexity 2023年第5期36卷 1927-1948页

作者： LU Jiajie WANG Yong FAN Yuan Department of Automation University of Science and Technology of ChinaHefei 230026China Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering and AutomationAnhui UniversityHefei 230601China

In this work,a novel gradient descent method based on event-triggered strategy has been proposed,which involves integer and fractional order ***,the convergence of integer order iterative optimization method and the stability of its associated system with integrator dynamics are *** on this result,a fractional order iteration approach has been developed by modelling the system with fractional order ***,to reduce the comsumption of computation,a feedback based event-triggered mechanism has been introduced to the gradient descent *** convergence of this new event-triggered optimization algorithm is guaranteed by using a Lyapunov method,and Zeno behavior is proved to be avoided ***,the effectiveness and advantages of the proposed algorithms are verified by numerical simulations.

关键词： Event-triggered mechanism fractional order iteration gradient descent Zeno behavior

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Distributed Optimization and Scaling Design for Solving Sylvester Equations

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Journal of Systems Science & Complexity 2024年第6期37卷 2487-2510页

作者： CHENG Songsong YU Xin ZENG Xianlin LIANG Shu HONG Yiguang Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering and AutomationAnhui UniversityHefei 230601China Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of TechnologyBeijing 100081China Department of Control Science and Engineering Tongji UniversityShanghai 201804China Shanghai Research Institute for Intelligent Autonomous Systems Tongji UniversityShanghai 201210China

This paper develops distributed algorithms for solving Sylvester *** authors transform solving Sylvester equations into a distributed optimization problem,unifying all eight standard distributed matrix *** the authors propose a distributed algorithm to find the least squares solution and achieve an explicit linear convergence *** results are obtained by carefully choosing the step-size of the algorithm,which requires particular information of data and Laplacian *** avoid these centralized quantities,the authors further develop a distributed scaling technique by using local information *** a result,the proposed distributed algorithm along with the distributed scaling design yields a universal method for solving Sylvester equations over a multi-agent network with the constant step-size freely chosen from configurable ***,the authors provide three examples to illustrate the effectiveness of the proposed algorithms.

关键词： Distributed optimization least squares solution linear convergence rate step-size interval Sylvester equation

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Optimal Cyber Attack Strategy Using Reinforcement Learning Based onCommon Vulnerability Scoring System

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Computer Modeling in engineering & Sciences 2024年第11期141卷 1551-1574页

作者： Bum-Sok Kim Hye-Won Suk Yong-Hoon Choi Dae-Sung Moon Min-Suk Kim Department of Electronic Information System Engineering Sangmyung UniversityCheonan31066Republic of Korea Department of Human Intelligence&Robot Engineering Sangmyung UniversityCheonan31066Republic of Korea Intelligent Convergence Research Laboratory Electronics and Telecommunications Research InstituteDaejeon34129Republic of Korea

Currently,cybersecurity threats such as data breaches and phishing have been on the rise due to the many differentattack strategies of cyber attackers,significantly increasing risks to individuals and *** technologies such as intrusion detection have been developed to respond to these cyber ***,advanced integrated cybersecurity that incorporates Artificial Intelligence has been the *** this paper,wepropose a response strategy using a reinforcement-learning-based cyber-attack-defense simulation tool to addresscontinuously evolving cyber ***,we have implemented an effective reinforcement-learning-basedcyber-attack scenario using Cyber Battle Simulation,which is a cyber-attack-defense *** scenarioinvolves important security components such as node value,cost,firewalls,and ***,we applieda new vulnerability assessment method based on the Common Vulnerability Scoring *** approach candesign an optimal attack strategy by considering the importance of attack goals,which helps in developing moreeffective response *** attack strategies are evaluated by comparing their performance using a variety ofReinforcement Learning *** experimental results show that RL models demonstrate improved learningperformance with the proposed attack strategy compared to the original *** particular,the success rateof the Advantage Actor-Critic-based attack strategy improved by 5.04 percentage points,reaching 10.17%,whichrepresents an impressive 98.24%increase over the original ***,the proposed method canenhance security and risk management capabilities in cyber environments,improving the efficiency of securitymanagement and significantly contributing to the development of security systems.

关键词： Reinforcement learning common vulnerability scoring system cyber attack cyber battle simulation

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Real-Time Imitation of human Head Motions, Blinks and Emotions by Nao robot: A Closed-Loop Approach 11

Real-Time Imitation of Human Head Motions, Blinks and Emotio...

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11th RSI International Conference on robotics and Mechatronics, ICRoM 2023

作者： Rayati, Keyhan Feizi, Amirhossein Beigy, Alireza Shahverdi, Pourya Masouleh, Mehdi Tale Kalhor, Ahmad Louie, Wing-Yue Geoffrey University of Tehran Human and Robot Interaction Lab Electrical and Computer Engineering Tehran Iran Shahrood University of Technology Department of Electrical Engineering Shahrood Iran Oakland University Intelligent Robotics Laboratory Michigan United States

ISBN: (纸本)9798350308105

This paper introduces a novel approach for enabling real-time imitation of human head motion by a Nao robot, with a primary focus on elevating human-robot interactions. By using the robust capabilities of the MediaPipe as a computer vision library and the DeepFace as an emotion recognition library, this research endeavors to capture the subtleties of human head motion, including blink actions and emotional expressions, and seamlessly incorporate these indicators into the robot's responses. The result is a comprehensive framework which facilitates precise head imitation within human-robot interactions, utilizing a closed-loop approach that involves gathering real-time feedback from the robot's imitation performance. This feedback loop ensures a high degree of accuracy in modeling head motion, as evidenced by an impressive R2 score of 96.3 for pitch and 98.9 for yaw. Notably, the proposed approach holds promise in improving communication for children with autism, offering them a valuable tool for more effective interaction. In essence, proposed work explores the integration of real-time head imitation and real-time emotion recognition to enhance human-robot interactions, with potential benefits for individuals with unique communication needs. © 2023 IEEE.

关键词： Emotion Recognition

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Effective distributed algorithm for solving linear matrix equations

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Science China(Information Sciences) 2023年第8期66卷 265-266页

作者： Songsong CHENG Jinlong LEI Xianlin ZENG Yuan FAN Yiguang HONG Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering and Automation Anhui University Department of Control Science and Engineering Tongji University Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of Technology

Dear editor,Solving linear matrix equations is a basic and important problem in many fields such as the computation of generalized inverses of matrices and(generalized) Sylvester equations. Also, the linear algebraic ...

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Event-Guided Attention Network for Low Light Image Enhancement

Event-Guided Attention Network for Low Light Image Enhanceme...

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International Joint Conference on Neural Networks (IJCNN)

作者： Qiaobin Wang Haiyan Jin Haonan Su Zhaolin Xiao Department of Computer Science and Engineering Engineering Research Center Shaanxi Key Laboratory for Network Computing and Security Technology Human-Machine Integration Intelligent Robot Shaanxi University Xi'an University of Technology

In the low-light conditions, images are corrupted by low contrast and severe noise, but event cameras can capture event streams with clear edge structures. Therefore, we propose an Event-Guided Attention Network for Low-light Image Enhancement (EGAN) using a dual branch Network and recover clear structure with the guide of events. To overcome the lack of paired training datasets, we first synthesize the dataset containing low-light event streams, low-light images, and the ground truth (GT) normal-light images. Then, we develop an end-to-end dual branch network consisting of a Image Enhancement Branch (IEB) and a Gradient Reconstruction Branch (GRB). The GRB branch reconstructs image gradients using events, and the IEB enhances low-light images using reconstructed gradients. Moreover, we develops the Attention based Event-Image Feature Fusion Module (AEIFFM) which selectively fuses the event and low-light image features using the spatial and channel attention mechanism, and the fused features are concatenated into the IEB and GRB, which respectively generate the enhanced images with clear structure and more accurate gradient images. Extensive experiments on synthetic and real datasets demonstrate that the proposed EGAN produces visually more appealing enhancement images, and achieves a good performance in structure preservation and denoising over state-of-the-arts.

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Multi-sensor-based Target Pose Estimation for Autonomous Precision Perching of Nano Aerial Vehicles 22

Multi-sensor-based Target Pose Estimation for Autonomous Pre...

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22nd International Conference on Control, Automation and Systems, ICCAS 2022

作者： Do, Truong-Dong Xuan-Mung, Nguyen Nguyen, Ngoc-Phi Lee, Ji-Won Lee, Yong-Seok Lee, Seok-Tae Hong, Sung-Kyung Sejong University Department of Convergence Engineering for Intelligent Drone Seoul05006 Korea Republic of Sejong University Faculty of Mechanical and Aerospace Engineering Seoul05006 Korea Republic of Korea Institute of Robotics and Technology Convergence Human-Robot Interaction Research Center Pohang37553 Korea Republic of Sejong University Department of Aerospace Engineering Seoul05006 Korea Republic of

ISBN: (纸本)9788993215243

Nano Aerial Vehicles have become widely used for a variety of complex missions due to their mobility and the ability to access hard-to-reach areas. In most cases, these tasks require the vehicles to land on ground targets or perch on platforms mounted on diverse surfaces. Considering the surface the vehicle will reach, controlling perching is obviously a challenging task. Besides, the reliability of target position and direction estimation has a significant impact on perching performance. In this paper, a multi-sensor-based target pose estimation for autonomous precision perching of nano drones is proposed. First, the perching target, a cube cage containing a small marker inside a larger one, is designed to enhance pose estimation capability at a wide range of distances. Second, we constructed a nano drone with an upward monocular camera and a 5-direction multi-ranger deck. Next, the flying vehicle's pose toward the perching target is calculated, followed by a Kalman filter for filtering and estimating the missing data. Finally, we introduced an algorithm to merge the pose data from multiple sensors when drones approach close to the target. Real measurements are conducted on the testbed. The experimental results demonstrated the utility and potential of the adopted approach with millimeter-level precision. © 2022 ICROS.

关键词： Drones

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Real-Time Imitation of human Head Motions, Blinks and Emotions by Nao robot: A Closed-Loop Approach

Real-Time Imitation of Human Head Motions, Blinks and Emotio...

引用

RSI/ISM International Conference on robotics and Mechatronics (ICROM)

作者： Keyhan Rayati Amirhossein Feizi Alireza Beigy Pourya Shahverdi Mehdi Tale Masouleh Ahmad Kalhor Wing-Yue Geoffrey Louie Human and Robot Interaction Lab Electrical and Computer Engineering University of Tehran Tehran Iran Department of Electrical Engineering Shahrood University of Technology Shahrood Iran Intelligent Robotics Laboratory Oakland University Michigan USA

This paper introduces a novel approach for enabling real-time imitation of human head motion by a Nao robot, with a primary focus on elevating human-robot interactions. By using the robust capabilities of the MediaPipe as a computer vision library and the DeepFace as an emotion recognition library, this research endeavors to capture the subtleties of human head motion, including blink actions and emotional expressions, and seamlessly incorporate these indicators into the robot’s responses. The result is a comprehensive framework which facilitates precise head imitation within human-robot interactions, utilizing a closed-loop approach that involves gathering real-time feedback from the robot’s imitation performance. This feedback loop ensures a high degree of accuracy in modeling head motion, as evidenced by an impressive R2 score of 96.3 for pitch and 98.9 for yaw. Notably, the proposed approach holds promise in improving communication for children with autism, offering them a valuable tool for more effective interaction. In essence, proposed work explores the integration of real-time head imitation and real-time emotion recognition to enhance human-robot interactions, with potential benefits for individuals with unique communication needs.

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Surface defect identification using Bayesian filtering on a 3D mesh

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Measurement: Sensors 2025年

作者： Dalle Vedove, Matteo Bonetto, Matteo Lamon, Edoardo Palopoli, Luigi Saveriano, Matteo Fontanelli, Daniele Department of Industrial Engineering Universitá di Trento Trento Italy DRIM Ph.D. of National Interest in Robotics and Intelligent Machines Italy Department of Information Engineering and Computer Science Universitá di Trento Trento Italy Human-Robot Interfaces and Interaction Istituto Italiano di Tecnologia Genoa Italy

This paper presents a CAD-based approach for automated surface defect detection. We leverage the a-priori knowledge embedded in a CAD model and integrate it with point cloud data acquired from commercially available stereo and depth cameras. The proposed method first transforms the CAD model into a high-density polygonal mesh, where each vertex represents a state variable in 3D space. Subsequently, a weighted least squares algorithm is employed to iteratively estimate the state of the scanned workpiece based on the captured point cloud measurements. This framework offers the potential to incorporate information from diverse sensors into the CAD domain, facilitating a more comprehensive analysis. Preliminary results demonstrate promising performance, with the algorithm achieving convergence to a sub-millimeter standard deviation in the region of interest using only approximately 50 point cloud samples. This highlights the potential of utilising commercially available stereo cameras for high-precision quality control applications. © 2025

关键词： Mesh generation

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