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检索条件"任意字段=3rd International Conference on Robotics, Control and Automation Engineering, RCAE 2020"

共 533 条记录，以下是41-50 订阅

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Wearable Piezoresistive Pressure Sensor Based on Graphene and PDMS Nanocomposite Sponge for Foot Steps Detection 3

Wearable Piezoresistive Pressure Sensor Based on Graphene an...

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3rd international conference on Computer, control and robotics, ICCCR 2023

作者： Owusu, Eric Kwame Djoulde, Aristide Liu, Mei Zhen, Jiang School of Mechatronic Engineering and Automation Shanghai University Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai200444 China

ISBN: (纸本)9781665492126

Wearable sensors based on nanocomposite sponge are gaining popularity due to their high sensitivity, stretchability and offer new opportunities for applications ranging from finger motion detection to soft robotics. However, when designing skin-Adaptable, mountable, or non-invasive wearable devices, several features need to be taken into account, such as stretchability, flexibility, sensitivity, and biocompatibility. Herein, we present a facile, cost-effective, flexible, and stretchable piezoresistive pressure sensor based on a graphene/polydimethylsiloxane (PDMS) nanocomposite sponge (GPNCS) for finger motion detection. The maximum sensitivities were 133.95 kPa-1 for the pressure range of 1-10 N and 5.05 kPa-1 for the pressure range of 10-20 N respectively. When various pressures are applied, the pressure response of the devices are highly stable, repeatable, and reversible. Moreover, the piezoresistive pressure sensor has excellent sensing performance at various pressures ranging from 1 to 20 N and is mechanically robust enough to work under repeated load without degradation. The proposed device demonstrated an exceptional ability to monitor small and large finger motion, which has numerous applications in the range of finger motion detection, flexible insole as wearable healthcare monitoring and gait monitoring device © 2023 IEEE.

关键词： Graphene

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Mobile Robot Path planning based on RRT-Reward Algorithm 24

Mobile Robot Path planning based on RRT-Reward Algorithm

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3rd international Symposium on control engineering and robotics, ISCER 2024

作者： Yan, Jun Huang, Zhen An, Ziqi School of Automation Wuhan University of Technology Wuhan430070 China

ISBN: (纸本)9798400709951

Aiming at the shortcomings of high randomness and low efficiency of the RRT algorithm, this paper proposes an improved RRT algorithm (RRT -Reward Algorithm). Firstly, the map is initialized, filled with concave obstacles, and inflated for processing. Then, the sampling strategy is improved by sampling eight nodes in a circular area, and choosing the best new nodes by calculating the reward value. It can direct the random tree to quickly grow in the direction of the objective. When the robot falls into a local optimal solution, the obstacle expansion boundary sampling strategy is adopted to quickly bypass the obstacles. Finally, the path is pruned and smoothed. This paper's algorithm has good performance through simulation experiments and data analysis comparison. © 2024 ACM.

关键词： Microrobots

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Fast and Robust Visual Loop Closure Detection based on MobileNetV3 and NetVLAD 24

Fast and Robust Visual Loop Closure Detection based on Mobil...

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3rd international Symposium on control engineering and robotics, ISCER 2024

作者： Ren, Mingrong Zhang, Xiurui Beijing University of Technology College of Automation Faculty of Information and Technology Beijing100124 China

ISBN: (纸本)9798400709951

Loop closure detection (LCD) is an important technique for maintaining global consistency in mobile robot simultaneous localization and mapping (SLAM) systems. However, the LCD problem remains a highly challenging under the interference of changes in perspective and appearance. To address this problem, a fast online LCD algorithm based on lightweight convolutional neural network MobileNetV3 and local feature aggregation layer NetVLAD is proposed. First, the cropped MobileNetV3 is used for image feature encoding. Second, the obtained feature is passed into the NetVLAD Layer and output the global features of the original image. Then, an approximate nearest neighbor method based hierarchical navigable small world (HNSW) is adopted to reduce feature matching time. Evaluation results on multiple publicly available datasets confirm that the proposed method significantly enhances computational efficiency while maintaining high accuracy. © 2024 ACM.

关键词： Convolutional neural networks

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3rd international Symposium on Industrial engineering and automation, ISIEA 2024

3rd International Symposium on Industrial Engineering and Au...

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3rd international Symposium on Industrial engineering and automation, ISIEA 2024

ISBN: (纸本)9783031704611

The proceedings contain 27 papers. The special focus in this conference is on Industrial engineering and automation. The topics include: A Collaborative robotics Application for the Assembly of Car Rear Lamps;An Excel Motion Profile Optimization (MoPO) Tool for 1-DOF Mechatronic Systems;adaptive Vibration Absorption Using Internal Model control Approach;Inverse Dynamics for Feedforward control of an Underactuated 6-DOF Gantry Crane;use of Lumped Mass Formulation in the Design Sensitivity Analysis of Flexible Multibody Dynamics;planning of Smart Charging Infrastructure for Electric Vehicles: An Italian Case Study;preliminary Design and Simulation of a Return Current Collector for an Innovative Trambus System;study on Energy Efficiency of an Electrified Railway Line;zero-Emission Heavy-Duty Trucks in Logistics: A Systematic Review and Analysis of Relevant Adoption Parameters;design and Development of an Autonomous Surface Vehicle for Supporting Underwater Navigation;local Path-Planning Optimisation for an Industrial Autonomous Mobile Robot via Dynamic Obstacle Detection;integrated Approach for Continuous Improvement of Robotic Garments Picking;sustainable Aspects of Intermodal Transport: A Systematic Literature Review on the Current State;application of Industry 4.0 Technologies for Transparency of Sustainability Data in Multi-tiered Manufacturing Supply Chains;artificial Intelligence in Small and Medium-Sized Enterprises: Requirements and Barriers;enablers, Barriers, and Opportunities for the Implementation of Circular Economy Practices in Small and Medium-Sized Enterprises: An Explorative Systematic Literature Review;Industry 5.0 and SMEs Future Work Competency Fields: A Literature Review;sustainability Assessment: A Complex Many-Objective Multi-Agent Multidisciplinary Problem;The Application of Collective System Design to Develop SME's;the Influence of Electric Vehicle Technologies on Environmental Sustainability in Industrial Reporting: A Bibliometric Study;automa

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3rd international Symposium on Industrial engineering and automation, ISIEA 2024

3rd International Symposium on Industrial Engineering and Au...

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3rd international Symposium on Industrial engineering and automation, ISIEA 2024

ISBN: (纸本)9783031704642

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Microfluidic Lab-on-a-Chip Design and Analysis for GFAP Biomarker Detection Using Machine Learning Algorithms 3

Microfluidic Lab-on-a-Chip Design and Analysis for GFAP Biom...

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3rd international conference on Communication, control, and Intelligent Systems, CCIS 2024

作者： Balaji, P. Devasena, D. Rithick, P. Karthi, T. Rishmitha, K. Roopa, R. Sri Ramakrishna Engineering College Dept. Of. Electronics and Instrumentation Engineering Tamilnadu Coimbatore India Sri Ramakrishna Engineering College Dept. of Robotics and Automation Engineering Tamilnadu Coimbatore India Kongu Engineering College Dept. of Automobile Engineering Erode India Science Arjun College of Technology Dept. of Artificial Intelligence and Data Tamilnadu Coimbatore India

ISBN: (纸本)9798331528201

The detection of Glial Fibrillary Acidic Protein (GFAP) is a crucial biomarker for diagnosing neurological conditions such as traumatic brain injuries (TBI) and astrocytoma. This paper presents the design and analysis of a novel microfluidic Lab-on-a-Chip (LoC) platform for GFAP biomarker detection. The system integrates microfluidic channels with embedded gold nanoparticle-functionalized electrochemical sensors for high-sensitivity biomarker capture. The microfluidic chip is designed using soft lithography and PDMS (Polydimethylsiloxane), allowing for precise fluid handling and minimal sample consumption. The electrochemical sensors generate voltage signals corresponding to GFAP concentration levels, which are processed using digital filtering techniques to mitigate noise and improve signal quality. For accurate biomarker quantification, machine learning algorithms, including Random Forest ,KNN and Support Vector Machine (SVM), are employed to classify sensor output data into clinically significant concentration ranges. The model is trained on both simulated and experimentally collected sensor data, achieving an accuracy of over 93 percent in distinguishing low (below 50 ng/mL) and high (above 50 ng/mL) GFAP concentrations. The combination of microfluidic precision, sensitive electrochemical detection, and data analysis enables rapid and reliable detection of GFAP , providing a scalable and portable solution for early diagnosis of brain injuries and neurodegenerative diseases. This system highlights the potential of integrating microfluidics and machine learning in next-generation point-of-care diagnostic devices. © 2024 IEEE.

关键词： Polydimethylsiloxane

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Model Optimization Methods for Real-time 2D Human Pose Estimation Applied to Robots: A Survey 3

Model Optimization Methods for Real-time 2D Human Pose Estim...

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3rd international conference on robotics, automation and Intelligent control, ICRAIC 2023

作者： He, Weiting Zeng, Bi Liu, Jianqi Ye, Xiaoying Zhou, Fuken Neusoft Institute Guangdong School of Computers Foshan China Guangdong University of Technology School of Computers Guangzhou China Guangdong University of Technology School of Automation Guangzhou China

ISBN: (纸本)9798350360813

With the development of technology, there are more and more robots around us. These robots need to recognize human pose to monitor people or interact with people, so the human pose estimation model is required to be deployed on robots to achieve these functions. Human pose estimation involves the processing of more than a dozen joints, so the human pose estimation model usually has a relatively large amount of calculation resulting in a relatively slow detection speed. Transferring these large amounts of image data to cloud servers for human pose estimation detection will consume a lot of network resources and detection time will be delayed. And the robot's local computing power is limited. So it is difficult to deploy both human pose estimation algorithm models and other complex algorithm models locally on robots. This requires various algorithms running locally on the robot to consume as little computing resources and real-time as possible. This paper studies the optimization methods of real-time 2D human pose estimation models studies in recent years, which are divided into three aspects: the optimization of the feature extraction network, the optimization of the joint point detection and the optimization of the joint point connection. And summarized the advantages and disadvantages of these optimization methods, attempting to apply them to robots. © 2023 IEEE.

关键词： Robots

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Speed and heading joint control of an unmanned sailboat based on NMPC 3

Speed and heading joint control of an unmanned sailboat base...

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3rd international conference on control and Intelligent robotics, ICCIR 2023

作者： Liu, Kai Yu, Jiancheng Zhao, Wentao Huang, Yan State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781510671867

Aiming at the strong coupling of speed and heading control of unmanned sailboats, this study proposes joint control of the speed and heading of unmanned sailboats based on nonlinear model predictive control (NMPC). Different from the existing control methods, which only carry out maximum speed and heading separation control, this study proposes the joint control of unmanned sailboats at any speed based on NMPC. A 4-DOF dynamic model of the unmanned sailboat is used, and the roll angle is limited to prevent the unmanned sailboat from capsizing. Simulation experiments prove that the proposed method can realize the speed and heading joint control of unmanned sailboats, and the roll angle can be limited, preventing the unmanned sailboat from capsizing. © 2023 SPIE.

关键词： Model predictive control

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Software design for GNSS aviation service performance assessment based on ionospheric scintillation 3

Software design for GNSS aviation service performance assess...

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3rd international conference on control and Intelligent robotics, ICCIR 2023

作者： Hu, Tieqiao Zhang, Gaojian Zhong, Lunlong School of Electronic Information and Automation Civil Aviation University of China Tianjin China

ISBN: (纸本)9781510671867

The analysis and evaluation of GNSS performance under ionospheric scintillation has always been a critical concern in the modern aviation industry. This paper based on the ionospheric scintillation model and satellite navigation solution principles, the GNSS aviation service performance assessment software is designed and applied to different ionospheric scintillation intensities, and the C/C++ language is used as the programming language to realize the GNSS aviation service performance assessment functions under different ionospheric scintillation environments and different flight phases. The software can flexibly change the ionospheric scintillation intensity, flight trajectory can be controlled. The software is highly scalable and reusable, and it is compatible with GPS/BDS/GALILEO/GLONASS systems. It fully considers the specification and visualization of the GNSS aviation service performance assessment under ionospheric scintillation conditions, promoting the improvement of the GNSS aviation service performance assessment system. © 2023 SPIE.

关键词： C++ (programming language)

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Research on Coal and Gangue Recognition Method Based on Improved MobileNetV3 Network 3

Research on Coal and Gangue Recognition Method Based on Impr...

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3rd international conference on Computer, control and robotics, ICCCR 2023

作者： Chen, Wei Wang, Shuang Luo, Qisheng Li, Xin College of Mechanical Engineering Anhui University of Science and Technology Collaborative Innovation Center of Mine Intelligent Equipment and Technology Huainan Anhui232001 China

ISBN: (纸本)9781665492126

In order to solve the problems of classical convolutional neural networks in coal and gangue recognition, such as large calculation amounts, slow recognition speeds, and low recognition accuracy, a method for coal and gangue recognition based on an improved MobileNetV3 network is proposed. Based on the MobileNetV3 model, the activation function and attention mechanism were improved and the number of network layers was reduced. Finally, the accuracy rate, F1 score, and floating-point operation (FLOPs) were used as the benchmark for model evaluation. The results showed that: The test accuracy rate after the improved MobileNetV3 is 99.5%, the F1 score is 0.995, and FLOPs is 50455003. A comparison between the model and the MobileNetV3, EfficientNet, and AlexNet models shows that all indicators of the model are higher than those of other models. Suitable for coal gangue recognition model. © 2023 IEEE.

关键词： Image enhancement

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