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检索条件"任意字段=18th International Conference on Control Automation Robotics and Vision"

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A Proposal of FPGA-Based Low Cost and Power Efficient Autonomous Fruit Harvester 6

A Proposal of FPGA-Based Low Cost and Power Efficient Autono...

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6th international conference on control, automation and robotics (ICCAR)

作者： Nilay, Kumar Mandal, Swarnabha Agarwal, Yash Gupta, Rishabh Patel, Manthan Kumar, Sumeet Shah, Poojan Dey, Sombit Annanya Indian Inst Technol Dept Chem Kharagpur W Bengal India Indian Inst Technol Dept Elect & Elect Commun Engn Kharagpur W Bengal India Indian Inst Technol Dept Elect Engn Kharagpur W Bengal India Indian Inst Technol Dept Geol & Geophys Kharagpur W Bengal India Indian Inst Technol Dept Mech Engn Kharagpur W Bengal India Indian Inst Technol Dept Aerosp Engn Kharagpur W Bengal India

ISBN: (纸本)9781728161396

In this paper, we present a power-efficient and low- cost prototype of a robotic harvester which employs multiple subsystems such as fruit detection, odometry, localization, profi- cient manipulation through computer vision, deep learning and a novel end-effector design. Fruit Plucking is performed using an end effector, and 3-degree of freedom (DOF) arm (made out of the integration of two linear actuators and a rotating platform) consolidated with a 4-wheeled differential drive mobile platform. Effective implementation of the visual processing is executed on the FPGA Fabric of the Xilinx PYNQ-Z2 Board, which accelerates Deep Neural Networks (DNNs) with improved Latency and Energy Efficiency as compared to a CPU or GPU based implementation.

关键词： autonomous vehicle autonomous fruit plucking control system object detection

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A Computer vision Aided Fabric Flaw Detection Method with Improved YOLO V5 Using the Attention Mechanism 22

A Computer Vision Aided Fabric Flaw Detection Method with Im...

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Proceedings of the 2022 4th international conference on robotics, Intelligent control and Artificial Intelligence

作者： Chaoyu Deng Changjiang Ju Guobiao Jin Lin Liu Guohuan Wang Department of Automation Shanghai Jiao Tong University China Consinee Group Co. Ltd. China School of Materials Science and Engineering Zhejiang Sci-Tech University China China Mobile Group Hubei Co. Ltd. China

ISBN: (纸本)9781450398343

A fabric quality detection method based on the YOLO architecture combined with the attention mechanism is proposed. A computer vision aided data acquisition system collects images for defect detection. the goal is to create clean samples for on-linear detection. the proposed method outperforms traditional flaw detection methods such as statistical analysis or heuristic algorithms in terms of accuracy and detection speed. A convolution neural network model is used to create the deep learning model, which detects defect features using cascade convolution layers and cross stage partial network structures as the backbone network. the leaky rectified linear unit and residual units are used to effectively determine and extract features. block attention module is used to improve detection accuracy even further. Fabric flaw quality detection can thus be kept effective and precise. For performance validation, an open source practically captured data set of fabric with and without flaws is used.

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Deep Learning Methods for Detecting and Classifying Brain Hemorrhages with Different Preprocessing Approach

Deep Learning Methods for Detecting and Classifying Brain He...

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RSI/ISM international conference on robotics and Mechatronics (ICROM)

作者： Mohammadmahdi Moazedi Mohammadhossein Feizi Nigjeh Alireza Jalouli Amirhossein Nikoofard Department of Control K.N Toosi University of Technology Tehran Iran

ISBN: (数字)9798331529734

ISBN: (纸本)9798331529741

Intracranial hemorrhage (ICH), resulting from trau-matic brain injuries, poses significant risks if not promptly diagnosed. the current standard involves manual CT scan examinations by radiologists, which can be time-consuming and prone to errors. To improve this, deep learning techniques are being explored for ICH detection. Recent AI advancements show promise in enhancing the accuracy and efficiency of diagnosing ICH and its subtypes. By utilizing large datasets and advanced algorithms, AI-based systems can aid in the timely identifi-cation and localization of hemorrhages, potentially improving patient outcomes through quicker interventions. In this work, we implemented multiple models such as ResNet18 (2D and 3D), ResNet50, VGG16 and the vision Transformer (ViT) architecture for classifying CT scans into Normal and Hemorrhage categories in a patient-wise manner. We utilized the MosMedData dataset, which includes 800 brain CT scans consisting of multiple series of DICOM images with and without signs of ICH. A significant challenge is that each patient has a different number of CT scan images, whereas our models require consistent depth across images. However, an advantage is that the CT scan images are sequentially ordered axially, allowing us to average pixel values and merge them into 3 and 10 channels. ResNet50 achieved the highest performance among our models using the 3-channel method, correctly identifying 278 out of 365 patients with brain hemorrhages and 270 out of 420 patients without hemorrhages.

关键词： Deep learning Solid modeling three-dimensional displays Accuracy Computed tomography Brain modeling Transformers Data models Hemorrhaging Residual neural networks

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Detection of Material on a Tray in an Automatic Assembly Line Based on Convolution Attention and Multitask Loss

Detection of Material on a Tray in an Automatic Assembly Lin...

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international conference on control and Robots (ICCR)

作者： Dunli Hu Yuting Zhang Xiaoping Zhang Xiangdong Zhang Beijing Key Laboratory of Fieldbus Technology and Automation North China University of Technology Beijing China

this paper proposes an end-to-end first-stage pallet detection algorithm with short training time and high detection accuracy based on the pre-detection staged material detection algorithm. Not only can it detect known materials, blank areas and fixed material areas on pallets, but also unknown and unwanted materials that are mixed and misplaced on pallets on automated assembly lines. It employs ResNet18 as the backbone network, incorporates the Convolutional Block Attention Module (CBAM) to improve model stability and accuracy, and optimizes the detection model using the multitask loss function based on Complete-IoU(CIoU) and cross entropy. the experimental results show that when compared to the original phased detection algorithm using YOLOv5s trained on four NVIDIA GeForce RTX 2080 Ti for 18 h, the phased detection algorithm used in this study's first stage material detection algorithm achieves 98% overall recognition accuracy, which is 7% higher than the original phased algorithm (91 % ). It also greatly reduces the model training time and allows rapid model deployment.

关键词： Training Lighting Multitasking Production facilities Stability analysis Robustness Entropy

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Collaborative Decision-Making Models for Flexible Manufacturing Robotic System

Collaborative Decision-Making Models for Flexible Manufactur...

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international conference on Modern Electrical and Energy Systems (MEES)

作者： Oleksandr Tsymbal Artem Bronnikov Department of Computer-Integrated Technologies Automation and Mechatronics Kharkiv National University of Radio Electronics Kharkiv Ukraine

ISBN: (纸本)9798350346848

the proposed paper analyses the modern approaches to collaborative robotics. there is proposed a new model of collaborative decision-making, based on predicate logics. the developed models use descriptions for group of robots, for workspace objects (monitored by distributed computer vision system), for problem-solving procedures, goals, specifications of robots and their sensor systems. While decision-making engine for single robot includes the problem- solver and operator schemes for actions, the decision-making for two robots will be defined by decision-making possibilities of each robot and role of robot in group. Here, the behavior of robot in a group will depend on technical specifications and ability to execute tasks of robotic team, of subordination and relations inside group, of workspace states specifications. Execution of tasks inside workspace of Flexible Manufacturing System is monitored by distributed computer vision system, which supplies additional feedbacks in general control system. Collaboration Decision-making Model is implemented by problem-solving software.

关键词： Computer vision Computational modeling Decision making Collaboration Robot sensing systems Problem-solving Task analysis

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the Search for Assembly Line Balance Oriented on the Allocation of Quality control Points 6

The Search for Assembly Line Balance Oriented on the Allocat...

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6th international conference on control, automation and robotics (ICCAR)

作者： Bysko, Sara Krystek, Jolanta Bysko, Szymon Majchrowicz, Krzysztof Silesian Tech Univ Fac Automat Control Elect & Comp Sci Gliwice Poland

ISBN: (纸本)9781728161396

In the paper important aspects of the proper use of quality inspection in production process were presented. the paper deals with the problem of assembly line balance analysis taking into account occurrence of quality checkpoints. It is very important for producers, because the earlier the detection of a defect, the greater the possibility of reducing the costs of manufacturing a faulty product. In order to present the real assembly line balancing problem taking into account occurrence of checkpoints in strictly defined places, simplified process of USB flash drive production was analyzed. the assembly line balancing was performed and the indicators evaluating quality of the solutions were obtained. the calculations were made for different conditions regarding the type and location of quality inspections. Approaches like classical assembly line balancing without and with quality inspection, and fragmentary assembly line balancing with quality inspection were verified. All results and discussion are presented in the paper.

关键词： assembly line balancing problem quality inspection heuristics quality indicators quality checkpoint

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A 3D-CLDNN Based Multiple Data Fusion Framework for Finger Gesture Recognition in Human-Robot Interaction

A 3D-CLDNN Based Multiple Data Fusion Framework for Finger G...

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international conference on control and Robots (ICCR)

作者： Wen Qi Haoyu Fan Yancai Xu Hang Su Andrea Aliverti School of Future Technology South China University of Technology Guangzhou China Institute of Automation Chinese Academy of Sciences Beijing China Dept. of Electronics Information and Bioengineering Politecnico di Milano Milano Italy

Finger gesture recognition using surface electromyography (sEMG) became an efficient Human-Robot Interaction (HRI) solution. Although Machine Learning (ML) techniques are widely applied in this field, the general solutions for labeling and collecting big datasets impose time-consuming implementation and heavy workloads. In this paper, a new deep learning structure, namely three-dimensional convolutional long short-term memory neural networks (3D-CLDNN) for finger gesture identification based on depth vision and sEMG signals, was proposed for human-machine interaction. It automatically labels the depth data by the self-organizing map (SOM) and predicts the hand gesture only adopting sEMG signals. the 3D-CLDNN method is integrated to improve the recognition rate and computational speed. the results showed the highest clustering accuracy (98.60%) and highest accuracy (84.40%) with the lowest computational time compared with different approaches. Finally, real-time human-machine interaction experiments are performed to demonstrate its efficiency.

关键词： Self-organizing feature maps Human computer interaction Neural networks Human-robot interaction Process control Gesture recognition Real-time systems

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Object relationship graph reasoning for object detection of remote sensing images 6

Object relationship graph reasoning for object detection of ...

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6th international conference on Image, vision and Computing, ICIVC 2021

作者： Li, Zeng Liu, Yifan Liu, Jingdong Yuan, Ye Raza, Asif Huo, Hong Fang, Tao Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (纸本)9781665443685

In recent years, deep learning has been successfully applied in object detection of remote sensing images, due to its powerful feature extraction and representation capabilities. However, it usually ignores the relationships among different objects, which greatly hinders the further improvement of the detection precision. In this paper, a novel object relationship graph reasoning deep learning method is proposed for object detection of remote sensing images. the reasoning is carried out on a graph constructed by region proposals. It mainly contains three modules for relationship generation in consideration of different factors that related to object detection: 1) Location-based relationship generation module (LRGM) aims to find spatial relationships among objects according to their positions;2) Feature-based relationship generation module (FRGM) focuses on mining semantic relationships among objects according to their appearances that implied in feature maps and 3) Class-dependent relationship generation module (CRGM) introduces class dependencies into the relationship generation process. Class dependencies are the prior knowledge that is obtained by computing class co-occurrence. the experimental results on a public twenty-class remote sensing object detection dataset have shown that the proposed method has better detection performance compared to other object detection deep networks with no considering object relationships. © 2021 IEEE.

关键词： Object detection

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Research on automatic cleaning method for hole work-piece in rail transit by intelligent robot 5

Research on automatic cleaning method for hole work-piece in...

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5th international conference on automation, control and robotics Engineering, CACRE 2020

作者： Li, Ronghua Wang, Zhidong Yang, Jingshan Lu, Qi DaLian JiaoTong University School of Mechanical Engineering School of Vehicle Engineering Dalian China

ISBN: (纸本)9781728198880

With the rapid development of rail transit in China, there is a great demand for the manufacturing capacity of intelligent equipment. Facing the demand of efficient cleaning of work-piece in rail transit, this paper proposes the automatic cleaning of assembly work-piece of railway vehicle by intelligent *** industrial robots are used to wash and deburr the work-piece in the intelligent processing. the main cleaning parts are fasteners, rods and valve bodies. the main technology is clean and deburr the work-piece with holes and deep holes. Binocular vision is used to identify the coarse positioning and fine positioning of the work-piece with holes, and different types of work-pieces are stored in memory, which is convenient for the robot to quickly clean and deburr. According to the coordinates of image processing holes, the robot can automatically optimize the path and complete the cleaning work. © 2020 IEEE.

关键词： Computer vision

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AMbit: An Efficient Pruning Technique in Federated Learning for Edge Computing Systems

AMbit: An Efficient Pruning Technique in Federated Learning ...

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IEEE international conference on Fog and Edge Computing (ICFEC)

作者： Emad Hammami Peiyuan Guan Amir Taherkordi Amin Shahraki Dapeng Lan University of Oslo Oslo Norway Shenyang Institute of Automation Shenyang China

ISBN: (数字)9798350361353

ISBN: (纸本)9798350361360

the Industrial Internet of things (IIoT) has revolutionized industrial sectors with enhanced connectivity, data exchange, and predictive maintenance. However, it faces various challenges from non-IID data distributions and communication overheads, to the consistency and privacy of prediction models for maintenance. Federated Learning (FL) has been considered as a prevalent technique to address privacy concerns. On the other hand, Edge computing (EC) is being increasingly introduced to ensure low-latency data processing in IIoT systems, especially those with time-critical requirements, e.g., industrial robotics and motion control systems. Moreover, the complexity and design dimensions of today’s IIoT systems has lead to the development of large Machine Learning (ML) models with millions of parameters, e.g., using computer vision for field management. this introduces computational and privacy challenges in IIoT scenarios. Innovative FL optimization approaches such as pruning are aimed to tackle these challenges by reducing the number of training parameters, while they may impact accuracy. In this paper, we propose a new technique, called Adaptive Mean aBsolute devIaTion (AMbit), which is an innovative pruning approach optimizing data transmission without compromising model accuracy and inducing additional computation overhead. By dynamically comparing the difference between the current and the previous weight value, AMbit adapts better to parameter fluctuations at different stages, thereby accurately locating those parameters that have less impact on convergence. AMbit’s generality and efficiency are illustrated using MNIST and CIFAR-10 datasets, outperforming traditional Magnitude pruning in FL. For MNIST, AMbit reduces data uploads up to 43.75%, with an increase of 0.62% in accuracy. While for For CIFAR-10, AMbit achieved a 63.44% decrease with a 5.22% drop in accuracy.

关键词： Training Privacy Accuracy Federated learning Service robots Computational modeling Data models Time factors Industrial Internet of things Edge computing

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