2022 18th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA)最新文献_第3页

Student Experiential Learning Projects in Agricultural Automation and Smart Farming 农业自动化和智慧农业学生体验式学习项目

2022 18th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA) Pub Date : 2022-11-28 DOI: 10.1109/MESA55290.2022.10004458

A. Nagchaudhuri, M. Mitra, J. Pandya, Caleb Nindo

引用次数: 0

Intelligent Industrial Controller Network Based on Modbus Fieldbus 基于Modbus现场总线的智能工业控制器网络

2022 18th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA) Pub Date : 2022-11-28 DOI: 10.1109/MESA55290.2022.10004466

Ching-Han Chen, Wenwen Chang, Meng-Yan Hsieh

{"title":"Intelligent Industrial Controller Network Based on Modbus Fieldbus","authors":"Ching-Han Chen, Wenwen Chang, Meng-Yan Hsieh","doi":"10.1109/MESA55290.2022.10004466","DOIUrl":"https://doi.org/10.1109/MESA55290.2022.10004466","url":null,"abstract":"Under the impetus of industrial internet of things and smart manufacturing applications, realizing the development and deployment of decentralized industrial controller networks is crucial. This paper designed an industrial controller network gateway, combined with the Grafcet virtual machine, for rapid development and deployment with distributed decentralized controller equipment. A gateway architecture with three microcontrollers for communication protocol management, Modbus device management, and real-time data collection and monitoring engines, respectively, was proposed in this study. The communication protocol management engine is responsible for transmission between the gateway and cloud system, thereby providing an implementation mechanism for cyber/physical system. The device management engine uses Modbus RTU to communicate with the underlying PLC controller. This engine comprises an IEEE1588 time synchronization protocol that provides PLC controller time stamping and synchronization mechanisms. The real-time data collection and monitoring engine is in charge with the data sorting management function, which is used to make the database management have efficient data query function. Finally, experiments were conducted on the basis of multiple PLC controllers and robotic arms motion control to validate this industrial controller network functional usability and performance.","PeriodicalId":410029,"journal":{"name":"2022 18th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA)","volume":"292 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117285225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Design of an All-Purpose Terrace Farming Robot 一种多功能梯田耕作机器人的设计

2022 18th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA) Pub Date : 2022-11-28 DOI: 10.1109/MESA55290.2022.10004400

Vibhakar Mohta, Adarsh Patnaik, S. K. Panda, Siva Vignesh Krishnan, Abhi Gupta, Abhay Shukla, Gauri Wadhwa, Shrey Verma, A. Bandopadhyay

{"title":"Design of an All-Purpose Terrace Farming Robot","authors":"Vibhakar Mohta, Adarsh Patnaik, S. K. Panda, Siva Vignesh Krishnan, Abhi Gupta, Abhay Shukla, Gauri Wadhwa, Shrey Verma, A. Bandopadhyay","doi":"10.1109/MESA55290.2022.10004400","DOIUrl":"https://doi.org/10.1109/MESA55290.2022.10004400","url":null,"abstract":"Automation in farming processes is a growing field of research in both academia and industries. A considerable amount of work has been put into this field to develop systems robust enough for farming. Terrace farming, in particular, provides a varying set of challenges, including reliable step climbing methods and stable navigation in unstructured terrains. We propose the design of a novel autonomous terrace farming robot, 'Aarohi’, that can effectively climb steep terraces of considerable heights. The design optimisation strategy for the overall mechanical structure is elucidated. Further, the embedded and software architecture are presented for a working prototype. The navigation strategy for autonomous traversal over the terrace steps using the scissor lift mechanism has also been discussed along with the experimental results for the controller. The adaptability of the design to specific operational requirements and modular farm tools allow 'Aarohi’ to be customised for a wide variety of use cases.","PeriodicalId":410029,"journal":{"name":"2022 18th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129985560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MESA 2022 Keynote Speeches MESA 2022主题演讲

2022 18th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA) Pub Date : 2022-11-28 DOI: 10.1109/MESA55290.2022.10004462

YangQuan Chen, Lichen Fu

引用次数: 0

A Soil Carbon Content Quantification Method Using A Miniature Millimeter Wave Radar Sensor and Machine Learning 基于微型毫米波雷达传感器和机器学习的土壤碳含量定量方法

2022 18th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA) Pub Date : 2022-11-28 DOI: 10.1109/MESA55290.2022.10004474

Di An, Yangquan Chen

{"title":"A Soil Carbon Content Quantification Method Using A Miniature Millimeter Wave Radar Sensor and Machine Learning","authors":"Di An, Yangquan Chen","doi":"10.1109/MESA55290.2022.10004474","DOIUrl":"https://doi.org/10.1109/MESA55290.2022.10004474","url":null,"abstract":"Soil carbon content plays an essential role in combating climate change, water cycling, and sustaining soil biodiversity. However, the conventional way of quantifying soil carbon content is labor intensive, lack of precision, slow, and costly. On large spatial scale, assessment of the effect of carbon (biochar) applied to the soil for soil health conditioning, remains to be very difficult. This paper for the first time demonstrates the viability using a millimeter-wave sensing method for quantifying soil carbon content. It can also distinguish biochar types from different biomass species. Furthermore, soil moisture monitoring, and biochar water retention capacity can also be quantified by utilizing the same miniature millimeter wave radar sensor empowered by machine learning. Specifically, in this study, we present our research materials, methodology, machine learning workflow, results, and the explanation and interpretation based on the physical principles of the millimeter wave radar array sensor in the context of soil carbon content. We validated our quantification method with supervised machine learning algorithm using real soil data collected in the field mixed with known biochar contents. The results show that our technique achieved a 95.7 per cent recognition accuracy across seven different biochar types. The work laid the foundation for future real-time, large spatial-scale evaluation and assessment of soil carbon content using biochar amendments or other related carbon-negative technologies. Thus, soil carbon content site-specific management can be made possible.","PeriodicalId":410029,"journal":{"name":"2022 18th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124632437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Advanced LARC Strategy of Maglev Planar Motor with GRU Neural Network Prediction 基于GRU神经网络预测的磁悬浮平面电机先进LARC策略

2022 18th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA) Pub Date : 2022-11-28 DOI: 10.1109/MESA55290.2022.10004479

Tiansheng Ou, Chuxiong Hu, Yu Zhu, Ming Zhang

{"title":"Advanced LARC Strategy of Maglev Planar Motor with GRU Neural Network Prediction","authors":"Tiansheng Ou, Chuxiong Hu, Yu Zhu, Ming Zhang","doi":"10.1109/MESA55290.2022.10004479","DOIUrl":"https://doi.org/10.1109/MESA55290.2022.10004479","url":null,"abstract":"To achieve high motion control accuracy and performance robustness simultaneously, this paper proposes an advanced learning adaptive robust control (LARC) strategy with gated recurrent unit (GRU) neural network prediction for magnetically levitated (maglev) planar motor. Adaptive model compensation and robust feedback control is firstly applied to guarantee robustness against parameter uncertainties and unknown disturbances. A GRU neural network is then trained with dataset collected from a practical maglev planar motor control system. The accurate predicted tracking error by the trained GRU neural network is compensated into the reference trajectory, which forms the proposed LARC strategy. Comparative experimental investigation validates that the proposed LARC strategy achieves comparable motion accuracy to iterative learning control (ILC) while avoiding undesired time-consuming iterations. Additionally, the proposed strategy outperforms ILC due to that satisfying control performance can be preserved even in the presence of trajectory variation, parameter uncertainty and unknown external disturbance.","PeriodicalId":410029,"journal":{"name":"2022 18th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126436838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Efficiency Evaluation on Different-shape Coils for Loosely Coupled Transformer of Wireless Power Transmission System 无线电力传输系统中松耦合变压器不同形状线圈的效率评价

2022 18th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA) Pub Date : 2022-11-28 DOI: 10.1109/MESA55290.2022.10004472

Yujie Lian, Tsung-Lin Lu, Yuang-Tung Cheng, Chih-Chi Lin, Jia-Wei Xu, J. Ho, Ming-Liang Lai

引用次数: 0

Deep Learning based Visual Object Recognition for Manipulator Grasps 基于深度学习的机械手抓取视觉对象识别

2022 18th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA) Pub Date : 2022-11-28 DOI: 10.1109/MESA55290.2022.10004478

Min-Fan Ricky Lee, Fu-Yao Hsu, Hoang-Phuong Doan, Quang-Duy To, Yavier Kristanto

{"title":"Deep Learning based Visual Object Recognition for Manipulator Grasps","authors":"Min-Fan Ricky Lee, Fu-Yao Hsu, Hoang-Phuong Doan, Quang-Duy To, Yavier Kristanto","doi":"10.1109/MESA55290.2022.10004478","DOIUrl":"https://doi.org/10.1109/MESA55290.2022.10004478","url":null,"abstract":"The visual object recognition using a CCD camera for manipulator grasps suffers from uncertainty (e.g., illumination, viewpoint, occlusion, and appearance). The conventional machine learning approach for classification requires a definite feature extraction before the model learning. The accuracy of feature extraction is affected in the presence of those uncertainties. A deep-learning-based approach for a manipulator is proposed (YOLOv4 framework, 167 layers) for the classification of various brands of condoms. This neural network architecture is improved based on the PRNet V3 and CSPNet which reduces computation without affecting the convergence of loss function during the learning. Three primary metrics (accuracy, precision, and recall) are used to evaluate the proposed model's prediction. The testing scenario includes the variation of working distance and viewpoint between the CCD camera and the object. The experiment results show the proposed Yolo v4 outperforms the other architectures (Yolo v3, Retina Net, ResNet-50, and ResNet-l0l).","PeriodicalId":410029,"journal":{"name":"2022 18th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA)","volume":"337 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133117563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Collaborative Robot Sensorization with 3D Depth Measurement System for Collision Avoidance 基于三维深度测量系统的协同机器人防撞传感器

2022 18th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA) Pub Date : 2022-11-28 DOI: 10.1109/MESA55290.2022.10004475

Maria Teresa Calcagni, C. Scoccia, Gianmarco Battista, G. Palmieri, M. Palpacelli

引用次数: 0

Spatial-Based Model Predictive Path Following Control for Skid Steering Mobile Robots 滑移转向移动机器人的空间模型预测路径跟踪控制

2022 18th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA) Pub Date : 2022-11-28 DOI: 10.1109/MESA55290.2022.10004456

Zhan Dorbetkhany, Alimzhan Murbabulatov, M. Rubagotti, A. Shintemirov

引用次数: 0