2020 International Conference on Advanced Robotics and Intelligent Systems (ARIS)最新文献_第2页

A paradigm of automatic ICT testing system development in practice ICT自动化测试系统开发的实践范例

2020 International Conference on Advanced Robotics and Intelligent Systems (ARIS) Pub Date : 2020-08-01 DOI: 10.1109/ARIS50834.2020.9205770

Shuhao Liang

{"title":"A paradigm of automatic ICT testing system development in practice","authors":"Shuhao Liang","doi":"10.1109/ARIS50834.2020.9205770","DOIUrl":"https://doi.org/10.1109/ARIS50834.2020.9205770","url":null,"abstract":"In-Circuit-Test (ICT) is an inevitable process for sustaining the quality of Printed Circuit Boards (PCBs) in the assembly and fabrication process. Applying automation to reduce labor and preventing errors in ICT has been studying by academics and industries for decades. Here we demonstrate a robot centric ICT testing system that integrates the peripheral equipment, also including the shop flow control system (SPCS). The graphic programming software – LabVIEW exploits to integrate robot arm, in-circuit test machine, PLC, HMI, and barcode reader. Communication among the facilities and error handling are the main challenges in the automated ICT system development. Heterogeneous communication protocols and third-party devices with unique syntax have caused some programming difficulties. The challenge of error handling is that it might be on hardware, software, or communication. Moreover, these errors may have occurred at 5-6 different facilities with chain effects. The robot arm dominates the main control sequence from the test start to finish. As a result, a steady automated ICT test system with real-time status monitoring has presented. That assists field personnel in eliminating problems quickly and promotes overall production line operation efficiency.","PeriodicalId":423389,"journal":{"name":"2020 International Conference on Advanced Robotics and Intelligent Systems (ARIS)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123848489","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

A Novel Robotic Grasp Detection Technique by Integrating YOLO and Grasp Detection Deep Neural Networks * 基于YOLO和抓取检测深度神经网络的机器人抓取检测技术*

2020 International Conference on Advanced Robotics and Intelligent Systems (ARIS) Pub Date : 2020-08-01 DOI: 10.1109/ARIS50834.2020.9205791

J. Yang, Ui-Kai Chen, Kai-Chu Chang, Ying-Jen Chen

{"title":"A Novel Robotic Grasp Detection Technique by Integrating YOLO and Grasp Detection Deep Neural Networks *","authors":"J. Yang, Ui-Kai Chen, Kai-Chu Chang, Ying-Jen Chen","doi":"10.1109/ARIS50834.2020.9205791","DOIUrl":"https://doi.org/10.1109/ARIS50834.2020.9205791","url":null,"abstract":"This paper proposes a robotic grasp detection technique by integrating you only look once (YOLO) deep neural network (DNN) and a grasp detection DNN. In this world, there are many people who cannot move their own bodies. The reason may be an accident or physical deterioration. So we need to invest more human resources to assist their lives. With new technological advances, robots are gradually able to perfectly replicate human movements. Hence, we intend to design a remote-control fetching robot. The system combines internet of things (IoT) technology, and users can use intelligent devices to control this robot with robotic arm to get the items they want. This paper focus on detecting the grasp of robotic arm by integrating YOLO and grasp detection DNNs. At first, YOLO V-v3 is applied to achieve object detection. Then a robotic grasp detection DNN is proposed to detect the robotic grasp. After that, the point cloud information of this object is utilized to calculate the normal vector of the grasp position such that the robotic arm can fetch the target along the normal vector. Finally, experiment results are given to show the practicality of the proposed robotic grasp detection Technique.","PeriodicalId":423389,"journal":{"name":"2020 International Conference on Advanced Robotics and Intelligent Systems (ARIS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124000129","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: 3

Design of Continuous-Time Sigma-Delta Modulator with Noise Reduction for Robotic Light Communication and Sensing 机器人光通信与传感用降噪连续σ - δ调制器的设计

2020 International Conference on Advanced Robotics and Intelligent Systems (ARIS) Pub Date : 2020-08-01 DOI: 10.1109/ARIS50834.2020.9205776

W. Lai

引用次数: 0

Model Predictive Control with Laguerre Function based on Social Ski Driver Algorithm for Autonomous Vehicle 基于社会滑雪驾驶员算法的自动驾驶汽车拉盖尔函数模型预测控制

2020 International Conference on Advanced Robotics and Intelligent Systems (ARIS) Pub Date : 2020-08-01 DOI: 10.1109/ARIS50834.2020.9205782

M. Elsisi

引用次数: 2

Implementation of the Camera-based Approach to Guide the Robot with Minimization Movements 基于摄像机的机器人最小运动引导方法的实现

2020 International Conference on Advanced Robotics and Intelligent Systems (ARIS) Pub Date : 2020-08-01 DOI: 10.1109/ARIS50834.2020.9205771

Hsin-Hsiung Huang, Juing-Huei Su, Chyi-Shyong Lee, Hsuan-Hao Li

引用次数: 0

Design of 1V CMOS 5.8 GHz VCO with Switched Capacitor Array Tuning for Intelligent Sensor Fusion 基于开关电容阵列调谐的1V CMOS 5.8 GHz VCO智能传感器融合设计

2020 International Conference on Advanced Robotics and Intelligent Systems (ARIS) Pub Date : 2020-08-01 DOI: 10.1109/ARIS50834.2020.9205785

W. Lai

引用次数: 0

Formation Control for Mobile Robot using Fuzzy - PI Controller 基于模糊PI控制器的移动机器人编队控制

2020 International Conference on Advanced Robotics and Intelligent Systems (ARIS) Pub Date : 2020-08-01 DOI: 10.1109/ARIS50834.2020.9205789

Min-Fan Ricky Lee, H.P.M Willybrordus, Sukamto, Sharfiden Hassen, Asep Nugroho

引用次数: 0

Object Detection using Transfer Learning for Underwater Robot 基于迁移学习的水下机器人目标检测

2020 International Conference on Advanced Robotics and Intelligent Systems (ARIS) Pub Date : 2020-08-01 DOI: 10.1109/ARIS50834.2020.9205774

Chia-Chin Wang, H. Samani

引用次数: 4

Intelligent Robot for Worker Safety Surveillance: Deep Learning Perception and Visual Navigation 用于工人安全监控的智能机器人:深度学习感知和视觉导航

2020 International Conference on Advanced Robotics and Intelligent Systems (ARIS) Pub Date : 2020-08-01 DOI: 10.1109/ARIS50834.2020.9205772

Min-Fan Ricky Lee, Tzu-Wei Chien

{"title":"Intelligent Robot for Worker Safety Surveillance: Deep Learning Perception and Visual Navigation","authors":"Min-Fan Ricky Lee, Tzu-Wei Chien","doi":"10.1109/ARIS50834.2020.9205772","DOIUrl":"https://doi.org/10.1109/ARIS50834.2020.9205772","url":null,"abstract":"The fatal injury rate for the construction industry is higher than the average for all industries. Recently, researchers have shown an increased interest in occupational safety in the construction industry. However, all the current methods using conventional machine learning with stationary cameras suffer from some severe limitations, perceptual aliasing (e.g., different places/objects can appear identical), occlusion (e.g., place/object appearance changes between visits), seasonal / illumination changes, significant viewpoint changes, etc. This paper proposes a perception module using end-to-end deep-learning and visual SLAM (Simultaneous Localization and Mapping) for an effective and efficient object recognition and navigation using a differential-drive mobile robot. Various deep-learning frameworks and visual navigation strategies with evaluation metrics are implemented and validated for the selection of the best model. The deep-learning model's predictions are evaluated via the metrics (model speed, accuracy, complexity, precision, recall, P-R curve, F1 score). The YOLOv3 shows the best trade-off among all algorithms, 57.9% mean average precision (mAP), in real-world settings, and can process 45 frames per second (FPS) on NVIDIA Jetson TX2 which makes it suitable for real-time detection, as well as a right candidate for deploying the neural network on a mobile robot. The evaluation metrics used for the comparison of laser SLAM are Root Mean Square Error (RMSE). The Google Cartographer SLAM shows the lowest RMSE and acceptable processing time. The experimental results demonstrate that the perception module can meet the requirements of head protection criteria in Occupational Safety and Health Administration (OSHA) standards for construction. To be more precise, this module can effectively detect construction worker's non-hardhat-use in different construction site conditions and can facilitate improved safety inspection and supervision.","PeriodicalId":423389,"journal":{"name":"2020 International Conference on Advanced Robotics and Intelligent Systems (ARIS)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121709562","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: 6

Autonomous Pose Correction and Landing System for Unmanned Aerial Vehicles 无人机自主姿态校正与着陆系统

2020 International Conference on Advanced Robotics and Intelligent Systems (ARIS) Pub Date : 2020-08-01 DOI: 10.1109/ARIS50834.2020.9205790

Min-Fan Ricky Lee, S. K., A. J.

{"title":"Autonomous Pose Correction and Landing System for Unmanned Aerial Vehicles","authors":"Min-Fan Ricky Lee, S. K., A. J.","doi":"10.1109/ARIS50834.2020.9205790","DOIUrl":"https://doi.org/10.1109/ARIS50834.2020.9205790","url":null,"abstract":"The landing is one of the most dangerous maneuvers in the entirety of the flight phase of an Unmanned Aerial Vehicle (UAVs). Sudden changes in the environment cause issues regarding the stability of the drone, which poses a difficult challenge in landing the UAV precisely. To better the safety of any UAVs flying in urban areas, UAVs should be landed carefully, in a GPS-denied or network-disconnected environment, by using vision and inertial data. This paper presents UAV safe landing system which comprises of three sub-systems for detection of designated landing sites and autonomous pose correction, landing site inspection and landing flight control. This paper deals with vision-based target detection and pose correction system in-depth. The airborne vision system is utilized to recognize certain markers on the landing site. The information from the onboard visual sensors and Inertial Measurement Unit (IMU) is utilized to control and land UAV in a perfect landing trajectory, on a precise location. A series of experiments have been outlined to test and optimize the proposed method using Parrot AR.Drone 2.0.","PeriodicalId":423389,"journal":{"name":"2020 International Conference on Advanced Robotics and Intelligent Systems (ARIS)","volume":"127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132054450","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