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12th International Electrical Engineering Congress, iEECON 2024

作者： Bootthanu, Nuttapong Prakaraphan, Chaiphan Boribun, Banyat Boupha, Phaiboon Thammawongsa, Nopparat Bhumiphan, Niwat Udon Thani Rajabhat University Department Of Smart Electronics And Electric Vehicles Udon Thani Thailand Udon Thani Rajabhat University Department Of Robotics And Automation Engineering Udon Thani Thailand

ISBN: (纸本)9798350383591

With the growing popularity of electric vehicles (EVs) and the integration of EV charging infrastructure into distribution systems, the need for efficient fault classification techniques has become paramount. This paper proposes a comprehensive approach for fault classifications in distribution systems that incorporate EV charging. To address this challenge, a fault classification algorithm by using differential evolution (DE) was proposed. By considering multiple data sources, including voltage measurements, and current waveforms, the algorithm achieves a high level of accuracy and robustness in fault classification. The results demonstrate its effectiveness in accurately identifying different fault types, including short circuits between line and line-to-ground faults. In conclusion, the proposed algorithm offers an effective solution for the accurate and fastly identification of various fault types, enabling utilities and operators to enhance the reliability and resilience of distribution systems in the context of increasing EV adoption. © 2024 IEEE.

关键词： Electric vehicles

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Prototype of an intelligent textile based robot skin to expand potential applications for cobot's 8

Prototype of an intelligent textile based robot skin to expa...

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8th International Conference on Mechanical Engineering and robotics Research, ICMERR 2023

作者： Niquet, Kirubagaran Patel, Nikunjkumar Schrodel, Frank Jahn, Matthias Varelmann, Sven Schmalkalden University of Applied Sciences Mechatronics and Robotics Thuringia Germany Schmalkalden University of Applied Sciences Automation Technology and Robotics Thuringia Germany ITP GmbH Engineers for Smart Textiles Germany B and R Industrie-Elektronik GmbH Germany

ISBN: (纸本)9798350330519

Human Robot Collaboration (HRC) already dates couple of decades back. However, as Industry 4.0 and 5.0 demands smart human-centered production technologies, more concerns has been raised in the recent years regarding optimization and adaption of collaborative robots (Cobots). The core idea of HRC focuses on sharing the same workspace and performing a joint task, by both robot and humans. This paper focuses mainly on the development of a textile-based cover for industrial robots, integrated by several sensor arrays, resulting in enabling the sense of the human in the vicinity of the robot. This information is further utilized to manipulate the standard robot control algorithm to facilitate a safe collaboration by adaptive speed control based on the distance of human and robot. Additionally, the cover material is based on textile, which in terms of passive safety, facilitates cushioning effect when collision is occurred. In terms of active safety, it provides a wide area of sensing with several proximity sensors integrated over the skin. © 2023 IEEE.

关键词： Intelligent robots

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3D vision helps to precisely palletize parts: Reliable part recognition allows the entire workflow to be optimized and automated, which speeds up palletizing, saves costs, and relieves employees

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PhotonicsViews 2024年第5期21卷 32-35页

作者： Molina, Jorge Laka, Kepa Gärtner, Jan smart and autonomous systems dept. Parke Teknologikoa C/ Iñaki Goenaga 5 Eibar Gipuzkoa20600 Spain automation and robotics area manager Magdalena Auzoa 2-B Berriatua Bizkaia48710 Spain Product Manager HALCON Arnulfstraβe 205 Munich80634 Germany

The Spanish robotics specialist Tekniker has developed a solution that allows chaotically lying parts to be automatically gripped from containers and placed in other containers in an orderly manner. The integrated MVT... 详细信息

关键词： 3D modeling

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Autonomous Navigation for Mobile Robots with Sensor Fusion Technology 2nd

Autonomous Navigation for Mobile Robots with Sensor Fusion T...

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2nd International Conference on Industry 4.0 and Advanced Manufacturing, I-4AM 2022

作者： Raja, Vikram Talwar, Dhruv Manchikanti, Akilesh Jha, Sunil Department of Robotics and Automation P.S.G College of Technology Tamil Nadu Coimbatore India IITD-AIA Foundation for Smart Manufacturing New Delhi India Department of Mechanical Engineering IIT Delhi New Delhi India

ISBN: (纸本)9789811905605

Autonomous navigation for a mobile robot is a method used to localize and navigate a robot in a random environment. The existing solution leverages a 2D LiDAR on top of the robot for mapping and obstacle avoidance but the LiDAR at times is unable to detect objects due to their size and color properties. This paper aims to develop a robot that uses both LiDAR and a vision-based sensor as fused sensor technology and to navigate the robot in any direction without any constraints using four-wheel Mecanum drive. The method uses 2D LiDAR as the global sensor for mapping and obstacle avoidance and fusing it with a vision sensor for detecting objects which cannot be detected by LiDAR. The vision sensor uses a deep learning object detection method;YOLOv3. Whenever an object is detected by a neural network the program sends a false laser scan, which is updated in the local cost map of the ROS navigation stack, which makes the robot aware of an object ahead and takes an alternate path to reach the goal position. This robot was tested in a gazebo ROS environment and was able to avoid obstacles irrespective of the size or color of the object. This method was successful in a simulation environment and can be used in industries that need 2D LiDAR as an affordable option and as a replacement for 3D LiDAR. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Mobile robots

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A surgical dataset from the da Vinci Research Kit for task automation and recognition

A surgical dataset from the da Vinci Research Kit for task a...

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2023 International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2023

作者： Rivas-Blanco, Irene Del-Pulgar, Carlos J. Pérez Mariani, Andrea Tortora, Giuseppe Reina, Antonio J. University of Malaga Dept. Systems Engineering and Automation Malaga Spain Scuola Superiore Sant'Anna The BioRobotics Institute Dept. Excellence in Robotics & Ai Pisa Italy ABzero Srl Smart Medical Theatre Lab Pisa Italy

ISBN: (纸本)9798350322972

The use of large datasets is essential in surgical robotics to advance in the field of recognition and automation of surgical tasks. Furthermore, public datasets allow the comparison of different algorithms and methods to evaluate their performance. The objective of this work is to provide a complete dataset of three common training surgical tasks performed with the da Vinci Research Kit (dVRK). The dataset contains a total of 206 trials performed by twelve different subjects. For each trial, the dataset includes 154 kinematic variables from the dVRK (both master and slave sides) together with the associated video recorded with a camera. All the data has been carefully timestamped and provided in a readable csv format. A MATLAB interface integrated with ROS for using and replicating the data is also provided. © 2023 IEEE.

关键词： Large dataset

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Vector Detection Network: An Application Study on Robots Reading Analog Meters in the Wild

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2021年第5期2卷 394-403页

作者： Dong, Zhipeng Gao, Yi Yan, Yunhui Chen, Fei The School of Mechanical Engineering and Automation Northeastern University Shenyang110819 China The Smart Manipulation Robots Laboratory Department of Mechanical and Automation Engineering T-Stone Robotics Institute The Chinese University of Hong Kong Hong Kong

Analog meters equipped with one or multiple pointers are wildly utilized to monitor vital devices' status in industrial sites for safety concerns. Reading these legacy meters autonomously remains an open problem since estimating pointer origin and direction under imaging damping factors imposed in the wild could be challenging. Nevertheless, high accuracy, flexibility, and real-time performance are demanded. In this work, we propose the vector detection network (VDN) to detect analog meters' pointers given their images, eliminating the barriers for autonomously reading such meters using intelligent agents like robots. We tackled the pointer as a 2-D vector, whose initial point coincides with the tip, and the direction is along tail-to-tip. The network estimates a confidence map, wherein the peak pixels are treated as vectors' initial points, along with a two-layer scalar map, whose pixel values at each peak form the scalar components in the directions of the coordinate axes. We established the Pointer-10 K dataset composing of real-world analog meter images to evaluate our approach due to no similar dataset is available for now. Experiments on the dataset demonstrated that our methods generalize well to various meters, robust to harsh imaging factors, and run in real-time. Impact Statement-From the automatic meter recognition perspective, this work proposes an accurate, flexible, and ready-to-use pointer detection algorithm that contributes to dealing with legacy pointer-type meters, which still serve in industrial sites to date and require extensive human inspection. Adopting this technique to automatic agents could save human labor and promote the effectiveness and efficiency of getting data. It also inspires particular object detection problems, wherein the object could be considered a vector whose position is concerned, and its orientation also matters. The proposed VDN is agnostic to the semantic meaning of the vectors it detects. Thus it could also be used to

关键词： Vectors

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A miniaturized mechanical antenna based on FEP/ THV unipolar electrets for extremely low frequency transmission

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Microsystems & Nanoengineering 2022年第3期8卷 223-233页

作者： Yong Cui Ming Wu Zhaoyang Li Xiao Song Chen Wang Haiven Yuan Zhi-Xin Yang Junwen Zhong School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China Department of Electromechanical Engineering and Centre for Artificial Intelligence and Robotics University of MacaoMacaoSAR 999078China School of Cyber Science and Technology Beihang universityBeijing 100191China State Key Laboratory of Internet of Things for Smart City and Department of Electromechanical Engineering University of MacaoMacaoSAR 999078China

An electret-based mechanical antenna(EBMA),which can transmit extremely low frequency(ELF)electromagnetic signals,has the advantages of miniaturization and high transmitting efficiency,with great potential applications in air,underwater,and underground *** improve the charge density of the electret,which is a key factor in determining the radiation performance of an EBMA,this work proposes a fluorinated ethylene propylene/terpolymer of tetrafluoroethylene,hexafluoropropylene and vinylidene fluoride(FEP/THV)unipolar electret exhibiting negative polarity,reaching a total charge density up to-0.46 mC/m^(2) for each layer of *** transmission distances can be achieved in sea water,soil,and air using a 3-layer-FEP/THV-based EBMA with a compact volume of 5×10^(-4) m^(3).As an application demonstration,binary ASili-coded ELF information of"BUAA"is successfully transmitted with a power consumption<5W.

关键词： antenna propylene FEP

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Fault Type Classification in the Electric Distribution System Integrated Electric Vehicle Charging Demand

Fault Type Classification in the Electric Distribution Syste...

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International Electrical Engineering Congress (iEECON)

作者： Nuttapong Bootthanu Chaiphan Prakaraphan Banyat Boribun Phaiboon Boupha Nopparat Thammawongsa Niwat Bhumiphan Department of Smart Electronics and Electric Vehicles Udon Thani Rajabhat University Udon Thani Thailand Department of Robotics and Automation Engineering Udon Thani Rajabhat University Udon Thani Thailand

ISBN: (数字)9798350383591

ISBN: (纸本)9798350383607

关键词： Fault diagnosis Voltage measurement Soft sensors Reliability engineering Electric vehicle charging Robustness Classification algorithms

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2D-3D Object Shape Alignment for Camera-Object Pose Compensation in Object-Visual SLAM

2D-3D Object Shape Alignment for Camera-Object Pose Compensa...

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IEEE International Conference on robotics and automation (ICRA)

作者： Hanyeol Lee Jae Hyung Jung Chan Gook Park Department of Aerospace Engineering and Automation and Systems Research Institute Seoul Natioinal University Seoul South Korea Smart Robotics Lab School of Computation Information and Technology Technical University of Munich Germany

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In this study, we propose an object shape alignment method through a robust optimization scheme for 6-degrees-of-freedom (DOF) object pose compensation. Although the pose estimation of the 3D object by the camera has been rapidly improved in recent years with the development of deep learning, the estimate still contains errors due to several factors. To compensate for this, we perform a shape alignment between the 2D segmentation of the object and the projection of the 3D object in the image plane. To avoid convergence to a local minimum in nonlinear optimization, we separate the pose into translation and rotation. This approach derives the optimization of a linear form in terms of a translation with reduced computational cost. For the rotation, the parallel optimization is performed with multiple initial values, reflecting to the uncertainty of an initial value. We formulate an invariant extended Kalman filter (EKF)-based object-visual simultaneous localization and mapping (SLAM) with a camera-object relative pose as the measurement model. To verify the performance of the proposed algorithm, we present the improved results of camera-object relative pose accuracy and localization and mapping accuracy in the several sequences of YCB-video dataset.

关键词： Location awareness Image segmentation Three-dimensional displays Simultaneous localization and mapping Uncertainty Accuracy Shape

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Encoding-Based Fusion Estimation for Multi-Sensor Systems: Applications to Target Tracking

Encoding-Based Fusion Estimation for Multi-Sensor Systems: A...

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Youth Academic Annual Conference of Chinese Association of automation (YAC)

作者： Kaiqun Zhu Qirong Tang Pengcheng Zhang State Key Laboratory of Internet of Things for Smart City University of Macau Macau China Laboratory of Robotics and Multibody System Tongji University Shanghai China School of Mechatronic Engineering and Automation Shanghai University Shanghai China

ISBN: (数字)9798350379228

ISBN: (纸本)9798350390780

This paper is concerned with the robust set-membership fusion estimation problem for multi-sensor systems under the influence of uncertain parameters and unknown-but-bounded noises. The sensor nodes and the remote fusion estimator are connected through bandwidth-constrained communication networks. To address this challenge, a dynamic encoding-based data compression mechanism is introduced, enabling the data to be efficiently encoded using a reduced number of bits. By thoroughly analyzing the impact of uncertain parameters, unknown-but-bounded noises, and data distortions on estimation performance, sufficient conditions are derived to ensure that the estimation error remains within the bounded ellipsoidal set. Moreover, the weight parameters are calculated by solving constrained optimization problems. Finally, the effectiveness of the proposed estimation algorithm is verified through a maneuvering target tracking scenario.

关键词： Sufficient conditions Estimation error Target tracking Noise Data compression Set theory Distortion

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