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检索条件"机构=Automation Robotics Technology"

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Natural Disaster Damage Assessment using Semantic Segmentation of UAV Imagery

Natural Disaster Damage Assessment using Semantic Segmentati...

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International Conference on robotics and Artificial Intelligence (ICRAI)

作者： Muhammad Haroon Asad Malik Muhammad Asim Muhammad Naeem Mumtaz Awan Muhammad Haroon Yousaf Computer Engineering Department University of Engineering and Technology Taxila Pakistan Swarm Robotics Lab National Center of Robotics and Automation University of Engineering and Technology Taxila Pakistan

Numerous natural disasters due to climate change pose major threats to the sustainability of public infrastructure and human lives. For emergency rescue and recovery during a disaster, a rapid and accurate evaluation of disaster damage is essential. In recent years, the Transformer has gained popularity in a number of tasks related to computer vision, which offers tremendous potential for improving the accuracy of disaster damage assessments. Our research aims to determine whether Vision Transformer (ViT) can be used to assess natural disaster damage on high-resolution Unmanned Aerial Vehicle (UAV) data in comparison with conventional deep-learning semantic segmentation techniques. We discuss if Transformer can perform better than CNNs in accurately assessing the damage caused in order to bridge the gap. Detailed performance comparison of state-of-art deep learning semantic segmentation models (UNET, Segnet, PSPNet, Deeplabv3+) and Transformer framework (SegFormer) for damage assessment is presented. The experimentation is performed on both natural disaster damage datasets (RescueNet, FloodNet). The study supported SegFormer as the most appropriate model for estimating disaster damage, with mIoUs of 96% on the RescueNet dataset and 82.22% on the FloodNet dataset, respectively. The Transformer is capable of outperforming conventional segmentation CNNs in understanding the entirety of the scene and assessing the severity of the damage, based on both quantitative evaluation and visual results.

关键词： Industries Visualization Semantic segmentation Public infrastructure Random access memory Transformers Autonomous aerial vehicles

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Iterative Optimal Feedback Control for Time-based Switched Systems

Iterative Optimal Feedback Control for Time-based Switched S...

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第43届中国控制会议

作者： Siying Qin Likang Song Gumin Jin Yuqing Chen Department of Mechatronics and Robotics School of Advanced Technology Xi’an Jiaotong-Liverpool University Department of Automation Shanghai Jiaotong University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

This paper presents a data-driven iterative optimal feedback control approach to solve input constrained nonlinear optimal control problems with switching *** consider multi-stage optimal control problems where the switching sequence of the stages is known but the switching instants and the control inputs are both unknown and are optimized. The proposed approach relies on repetitive task execution and assumes inexact models for both continuous and discrete dynamics in between and at the switching events. Our key contribution is a novel algorithm which iteratively computes a local feedback control input along the measured trajectory of the controlled system, as opposed to computing the control input along the trajectory predicted by an inexact model. We conjecture that the proposed algorithm can significantly reduce the cost compared to alternative methods that use model-based future prediction. The benefit of the algorithm is illustrated with numerical simulation and demonstrated with an experiment using a three-link torque-controlled robot. The algorithm provides a new systematic method for real-time data-driven optimal control of complex robotic systems.

关键词： red Iterative Optimal Feedback Control for Time-based Switched Systems

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An Improved Method for Identifying SSVEP Single-Channel Target Frequency for BCI Systems 3

An Improved Method for Identifying SSVEP Single-Channel Targ...

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3rd International Conference on Emerging Trends in Electrical, Control, and Telecommunication Engineering, ETECTE 2024

作者： Wahab, Adeel Khan, Umar Shahbaz Nawaz, Tahir Akbar, Hassan Shah, Syed Tayyab Hussain Khan, Zulfiqar Hasan Department of Mechatronics Engg National University of Sciences and Technology IslamabadH-12 Pakistan National Centre of Robotics and Automation National University of Sciences and Technology IslamabadH-12 Pakistan Department of Basic Sciences and Humanities National University of Sciences and Technology IslamabadH-12 Pakistan Faculty of Electrical Engineering Ghulam Ishaq Khan Institute of Engineering Sciences and Technology Topi Pakistan

ISBN: (纸本)9798331504861

Steady-state visual evoked potential (SSVEP) is a brain response to flickering visual stimuli in the occipital area. SSVEP detection methods usually utilize multichannel configurations, which can be intricate and uncomfortable. Conversely, single-channel configurations are simple and user-friendly, but attaining the necessary accuracy is difficult, requiring the development of a reliable and accurate single-channel technique. For this purpose, this study introduces a hybrid VMD-LGBM approach to single-channel SSVEP detection. The proposed technique utilizes VMD as a preprocessor that decomposes the SSVEP signal into harmonic, intrinsic mode functions (IMFs), and noisy IMFs. To separate harmonic IMFs from noisy IMFs, the crest factor of the cross-correlation function was introduced. Then the restored SSVEP signal is used to extract features using power spectral density analysis (Welch's approach), followed by classification using a Light Gradient Boosting Machine. Our proposed approach performs better than available techniques in terms of classification accuracy and information transfer rate. The proposed single-channel approach provides better efficiency and user-friendliness for practical applications. ©2024 IEEE.

关键词： Harmonic analysis

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3D-DAT: 3D-Dataset Annotation Toolkit for Robotic Vision

3D-DAT: 3D-Dataset Annotation Toolkit for Robotic Vision

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IEEE International Conference on robotics and automation (ICRA)

作者： Markus Suchi Bernhard Neuberger Amanzhol Salykov Jean-Baptiste Weibel Timothy Patten Markus Vincze Vision for Robotics Laboratory Automation and Control Institute TU Wien Vienna Austria Robotics Institute University of Technology Sydney Sydney Australia

Robots operating in the real world are expected to detect, classify, segment, and estimate the pose of objects to accomplish their task. Modern approaches using deep learning not only require large volumes of data but also pixel-accurate annotations in order to evaluate the performance and therefore safety of these algorithms. At present, publicly available tools for annotating data are scarce and those that are available rely on depth sensors, which excludes their use for transparent, metallic, and general non-Lambertian objects. To address this issue, we present a novel method for creating valuable datasets that can be used in these more difficult cases. Our key contribution is a purely RGB-based scene-level annotation approach that uses a neural radiance field-based method to automatically align objects. A set of user studies demonstrates the accuracy and speed of our approach over a purely manual or depth sensor assisted pipeline. We provide an open-source implementation of each component and a ROS-based recorder for capturing data with a eye-in-hand robot system. Code will be made available at https://***/markus-suchi/3D-DAT.

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Hybrid6D: A Dual-Stream Transformer-CNN Approach for 6D Object Pose Estimation from RGB-D Images

Hybrid6D: A Dual-Stream Transformer-CNN Approach for 6D Obje...

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2023 IEEE International Conference on robotics and Biomimetics, ROBIO 2023

作者： Fu, Songhao Zhang, Qiang Sun, Xueying Liu, Mingmin Fan, Zhiwei Jiangsu University of Science and Technology College of Automation No. 666 Changhui Road Zhenjiang212100 China Central Research Institute Siasun Robot & Automation CO. Ltd. No. 33 Quanyun Road Hunnan District Shenyang110167 China Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9798350325706

Estimating the 6D pose presents a formidable challenge due to the intricate nature of world objects and the myriad of issues encountered when acquiring data from real-world scenes. This task is compounded by occlusions, fluctuating lighting conditions, and data noise. The conventional CNN or Transformer architectures primarily focus on either local or global information, and relying solely on RGB or depth maps proves inadequate for extracting the most robust features. To address these challenges, we introduce Hybird6D, a dual-stream fusion deep learning approach that leverages a Transformer-CNN structure and attention mechanism to regress the 6D pose of objects using RGB-D images as input. Our methodology capitalizes on the strengths of both Transformers and CNNs, incorporating global attention and local attention, while simultaneously harnessing both appearance and geometric information. This comprehensive approach enhances robustness to noise and occlusions. Experimental evaluation on the YCB-Video dataset demonstrates that our method outperforms existing Transformer-based approaches and surpasses advanced CNN-based methods. Moreover, our approach exhibits superior performance on symmetry objects, textureless objects, and small objects. © 2023 IEEE.

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SmartDrive: The Ultimate EV-IoT Control Hub 5

SmartDrive: The Ultimate EV-IoT Control Hub

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Elavarasi, K. Rajasekar, P. Chandra Mouli, B. Siva Subramanian, P. Bharanidharan, R. Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore India Dhanalakshmi Srinivasan College of Engineering Department of Electrical and Electronics Engineering Coimbatore India Dhaanish Ahmed Institute of Technology Department of Robotics and Automation India SSM Institute of Engineering and Technology Department of Electrical and Electronics Engineering Engineering India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518097

IoT technology is crucial for advancing smart automation in electric vehicles (EVs). Interest in smart grids has surged due to the growth of distributed generation, the aging infrastructure of the existing grid, and the push for network modernization. As accurate usage predictions gain importance, so does the need for efficient energy storage systems, particularly for electrical energy. Green electrical energy storage systems are common in EVs due to their excellent electrical properties and adaptability, but the grid faces challenges from potential battery damage caused by overcharging, deep discharging, and high load surges. In this project, hardware utilizing GPS and IoT technology was developed to monitor and manage both the EV and its battery systems. A Battery Management System (BMS) with real-time monitoring and GPS tracking capabilities estimates the State of Charge (SoC) using coulomb counting techniques, alongside an MQTT protocol for message-based communication. The proposed BMS is implemented on a hardware platform utilizing the Arduino environment, a central processor, suitable sensing technologies, and interface devices. Project code was developed in the Arduino IDE and applied to the hardware system. © 2024 IEEE.

关键词： Battery management systems

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Frequency shift-based 3D shape measurement in the presence of strong inter-reflection

Frequency shift-based 3D shape measurement in the presence o...

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2024 International Conference on Optical and Photonic Engineering, icOPEN 2024

作者： Miao, Huisi Liu, Wan Yan, Ziyu Xu, Weidong Zhang, Dongbo Zhang, Hui School of Automation and Electronic Information Xiangtan University Xiangtan411105 China Xiangtan Electric Manufacturing Co..Ltd. Xiangtan411105 China National Engineering Research Center of Robot Visual Perception and Control Technology School of Robotics Hunan University Changsha410082 China

ISBN: (数字)9781510688117

ISBN: (纸本)9781510688100

Traditional fringe projection profilometry (FPP) struggles with robust imaging and high-precision three-dimensional (3D) reconstruction in complex lighting environments with strong inter-reflection. This paper proposes a method combining frequency-shifted fringe projection and epipolar constraint to achieve high-precision 3D profilometry in such scenarios. First, environmental light interference is mitigated and the maximum fringe frequency is reduced through window function analysis (WFA) and a four-step phase-shifting (PS). Then, direct and indirect illumination are separated in the Fourier domain. Direct illumination positions are identified using threshold segmentation and epipolar geometry constraint, enhancing imaging robustness in strong inter-reflection contexts. Experimental results demonstrate that the method constructs direct and indirect illumination feature models, accurately obtaining direct illumination information and eliminating multi-path interference. Additionally, in tests with complex geometrical surfaces, imaging efficiency and robustness significantly improve, with effective point cloud data increasing by over 30% and planar accuracy improving by 50%. © 2025 SPIE.

关键词： Light interference

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Design and Characterization of a Novel, Low-Cost Resistive Stretch Sensor Band for Interactions in HCI Applications 15

Design and Characterization of a Novel, Low-Cost Resistive S...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Sankar, B. Dhuraisamy, Arun Kumar Sindhu, S. Rajath, S. Dept. of Mechanical Engineering Bangalore560012 India PSG College of Technology Dept. of Robotics & Automation Engineering Coimbatore641004 India Dept. of Electronics Engineering Chennai600127 India Dept. of Design and Manufacturing Bangalore560012 India

ISBN: (纸本)9798350370249

In recent years, Human-Computer Interaction (HCI) technology has progressed significantly toward creating immersive experiences. Yet, the physical interaction within these environments often relies on hand controllers or hand tracking systems using gestures that lack the naturalistic feeling of grasping objects and are computationally expensive, respectively. This study introduces the Resistive Stretch Sensor (RSS) Band, a novel approach to bridging this gap by enabling interactions in HCI through the use of conductive rubber. The RSS is designed as a wearable band that conforms to the user's forearm, responding to muscle expansion during a full power grasp and release motion. As the RSS band stretches, its resistance value changes, which can be used as input for interactions within a virtual environment. The research characterises the conductive rubber material to determine the optimal RSS band design. The design process of the RSS band is detailed, considering ergonomic factors and the need for reliable and repeatable sensor output. A calibration method is developed to translate resistance changes into accurate input for HCI systems. The study concludes by examining the RSS's potential applications, such as in human-computer interaction. © 2024 IEEE.

关键词： Conductive Rubber Human-Computer Interaction Resistive Stretch Sensor

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Application of Deep Learning Method to Estimate Bottomhole Pressure Dynamics of Oil Wells 32

Application of Deep Learning Method to Estimate Bottomhole P...

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32nd IEEE International Symposium on Industrial Electronics, ISIE 2023

作者： Cheng, Haibo Li, Shichao Zeng, Peng Vyatkin, Valeriy State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China Luleå University of Technology Electrical and Space Engineering Department of Computer Science Luleå Sweden Aalto University Department of Electrical Engineering and Automation Helsinki Finland

ISBN: (纸本)9798350399714

Surrogate models, which have become an effective and popular method to close loop reservoir management problems, use a data-driven approach to predict dynamic injection and production wells parameters and optimize waterflooding development. In this study, a deep learning-based surrogate model method is proposed to estimate bottomhole pressure (BHP) of production wells in waterflooding reservoirs. Bidirectional long short-term memory (BiLSTM) network, as an efficient deep learning approach, is applied to BHP estimation using fluctuation data. Extended Fourier amplitude sensitivity test (EFAST) method is employed to analyse the influence of different input factors on BHP dynamics, and a reduced dataset is rebuilt to predict BHP parameter based on BiLSTM-EFAST algorithm. The estimation results are tested on a dataset from Volve oilfield in North Sea, and compared with other deep learning methods. The test results indicate that the proposed method can achieve higher prediction accuracy. A reduced dataset-based approach provides a new attempt to reduce model complexity and improve calculation speed for big data-driven surrogate model in oil and gas industry. © 2023 IEEE.

关键词： Long short-term memory

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LabView based Automated Motor Test Bench for Induction Motors

LabView based Automated Motor Test Bench for Induction Motor...

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International Conference on Computing, Mathematics and Engineering Technologies (iCoMET)

作者： Muhammad Sohaib Hamza Shaukat Tauseef Tauqeer Arslan Shahid Usman Younis Rehan Hafiz Faculty of Engineering Information Technology University Lahore Pakistan Industrial Monitoring and Automation Lab National Center of Robotics and Automation Pakistan

Induction motors comprise more than 90 percent of the industrial load. With time, Induction motors are prone to losses. To save energy consumption, predictive maintenance of motors must be carried out at regular intervals. The industrial monitoring and automation lab has developed state-of-the-art motor test bench facility which is completely automated using LabView - a widely used industrial software. The manual methods of motor testing are not only hectic but also unreliable. A systematic approach has been adopted to measure and analyze various parameters of the induction motor, which will help us to identify key performance factors. This work is towards the development of a high performance motor test bench facility for industrial load. In its current state it can measure up to 15 hp induction motors and perform the tests such as No-Load Test, Full-Load Test, Locked-Rotor Test, Temperature Rise Test, DC Winding Test etc.

关键词： Temperature measurement Temperature distribution Induction motors Systematics Current measurement Windings DC motors

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