2020 5th International Conference on Robotics and Automation Engineering (ICRAE)最新文献_第2页

Improved YOLO-V3 Workpiece Detection Method for Sorting 改进的YOLO-V3工件检测分选方法

2020 5th International Conference on Robotics and Automation Engineering (ICRAE) Pub Date : 2020-11-20 DOI: 10.1109/ICRAE50850.2020.9310804

Jinmin Peng, Wenyu Liu, Tongfei You, Binglong Wu

{"title":"Improved YOLO-V3 Workpiece Detection Method for Sorting","authors":"Jinmin Peng, Wenyu Liu, Tongfei You, Binglong Wu","doi":"10.1109/ICRAE50850.2020.9310804","DOIUrl":"https://doi.org/10.1109/ICRAE50850.2020.9310804","url":null,"abstract":"Aiming at the problems of insufficient workpiece identification and detection accuracy in industrial scenes and difficulty in positioning multiple workpieces, a detection algorithm for identifying workpieces is proposed. Based on the YOLO-V3 algorithm, the network structure and multi-scale detection are improved, and the idea of hollow convolution is introduced. The residual dense block is used to replace the residual block in the original algorithm and combined with the convolutional layer to enhance the network's extraction of workpiece feature information. The original 3 scale detection is increased to 5 scale detection to improve the detection ability of small objects, Through the hollow convolution to expand the feature map of the workpiece to assist the network to extract deep-level object features, use the Kinect v2 sensor to collect the image of the workpiece and make it into a data set, the improved algorithm is tested on the data set, the experimental results show: The average detection accuracy of the workpiece reaches 92.98%, which is about 5% higher than the accuracy of the original algorithm. The combination of this method and robot grasping technology can replace manual labor to effectively complete the sorting of workpieces in industrial scenes.","PeriodicalId":296832,"journal":{"name":"2020 5th International Conference on Robotics and Automation Engineering (ICRAE)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127511286","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

Efficient Path Planning and Real-Time Obstacle Avoidance for Mobile Robots 移动机器人的有效路径规划与实时避障

2020 5th International Conference on Robotics and Automation Engineering (ICRAE) Pub Date : 2020-11-20 DOI: 10.1109/ICRAE50850.2020.9310866

Siyu Wan

引用次数: 1

On the Reconfiguration of Cable-Driven Parallel Robots with Multiple Mobile Cranes 基于多移动起重机的缆索驱动并联机器人重构研究

2020 5th International Conference on Robotics and Automation Engineering (ICRAE) Pub Date : 2020-11-20 DOI: 10.1109/ICRAE50850.2020.9310900

Hor Tan, Latifah Nurahmi, B. Pramujati, S. Caro

引用次数: 9

Trajectory Planner and Motion Control Solution for Automated Valet Parking Function 自动代客泊车功能的轨迹规划与运动控制解决方案

2020 5th International Conference on Robotics and Automation Engineering (ICRAE) Pub Date : 2020-11-20 DOI: 10.1109/ICRAE50850.2020.9310803

Z. Lipeng, Zhu Haifeng, Barbe David, Yang Xin, Chen Ting

引用次数: 0

Brain Computer Interface for Neurorehabilitation with Kinesthetic Feedback 基于动觉反馈的神经康复脑机接口

2020 5th International Conference on Robotics and Automation Engineering (ICRAE) Pub Date : 2020-11-20 DOI: 10.1109/ICRAE50850.2020.9310801

Adithya K, Saurabh Jacob Kuruvila, Sarang Pramode, Niranjana Krupa

引用次数: 3

Accuracy Evaluation of a Lightweight Analytic Vehicle Dynamics Model for Maneuver Planning* 机动规划轻量化分析车辆动力学模型的精度评价*

2020 5th International Conference on Robotics and Automation Engineering (ICRAE) Pub Date : 2020-11-20 DOI: 10.1109/ICRAE50850.2020.9310898

J. Ziehn, M. Ruf, M. Roschani, J. Beyerer

{"title":"Accuracy Evaluation of a Lightweight Analytic Vehicle Dynamics Model for Maneuver Planning*","authors":"J. Ziehn, M. Ruf, M. Roschani, J. Beyerer","doi":"10.1109/ICRAE50850.2020.9310898","DOIUrl":"https://doi.org/10.1109/ICRAE50850.2020.9310898","url":null,"abstract":"Models for vehicle dynamics play an important role in maneuver planning for automated driving. They are used to derive trajectories from given control inputs, or to evaluate a given trajectory in terms of constraint violation or optimality criteria such as safety, comfort or ecology. Depending on the computation process, models with different assumptions and levels of detail are used; since maneuver planning usually has strong requirements for computation speed at a potentially high number of trajectory evaluations per planning cycle, most of the applied models aim to reduce complexity by implicitly or explicitly introducing simplifying assumptions. While evaluations show that these assumptions may be sufficiently valid under typical conditions, their effect has yet to be studied conclusively. We propose a model for vehicle dynamics that is convenient for maneuver planning by supporting both an analytic approach of extracting parameters from a given trajectory, and a generative approach of establishing a trajectory from given control inputs. Both applications of the model are evaluated in real-world test drives under dynamic conditions, both on a closed-off test track and on public roads, and effects arising from the simplifying assumptions are analyzed.","PeriodicalId":296832,"journal":{"name":"2020 5th International Conference on Robotics and Automation Engineering (ICRAE)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129323473","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

Biological Optical AI and Bionic Optical AI 生物光学AI和仿生光学AI

2020 5th International Conference on Robotics and Automation Engineering (ICRAE) Pub Date : 2020-11-20 DOI: 10.1109/ICRAE50850.2020.9310880

A. Liang

引用次数: 1

Combining Vision Sensing with Knowledge Database for Environmental Modeling in Dual-arm Robot Assembly Task 结合视觉感知与知识库的双臂机器人装配环境建模

2020 5th International Conference on Robotics and Automation Engineering (ICRAE) Pub Date : 2020-11-20 DOI: 10.1109/ICRAE50850.2020.9310867

Zhengwei Wang, Yahui Gan, X. Dai, Bo Zhou, Fang Fang

引用次数: 2

2020 5th International Conference on Robotics and Automation Engineering (ICRAE) Pub Date : 2020-11-20 DOI: 10.1109/icrae50850.2020.9310889

引用次数: 0

Health Monitoring of Flexible Structures Via Surface-mounted Microsensors: Network Optimization and Damage Detection 基于表面微传感器的柔性结构健康监测:网络优化和损伤检测

2020 5th International Conference on Robotics and Automation Engineering (ICRAE) Pub Date : 2020-11-20 DOI: 10.1109/ICRAE50850.2020.9310827

S. Mariani, S. E. Azam

{"title":"Health Monitoring of Flexible Structures Via Surface-mounted Microsensors: Network Optimization and Damage Detection","authors":"S. Mariani, S. E. Azam","doi":"10.1109/ICRAE50850.2020.9310827","DOIUrl":"https://doi.org/10.1109/ICRAE50850.2020.9310827","url":null,"abstract":"Flexible and composite structures often develop hidden damages, e.g. cracks or delamination between laminae or phases. Such events could be sensed through embedded structural health monitoring (SHM) systems, but past experimental studies in the literature proved that embedding sensors for SHM purposes may decrease the reliability of the structure, as the sensor acts as an inclusion. In former studies, the authors proposed to adopt a surface-mounted SHM approach based on inertial MEMS (micro electro-mechanical systems) sensors, which has two advantages: it is low cost and also it avoids the afore-mentioned detrimental effects on the endurance limit state of the structure. However, the low accuracy of the MEMS sensors and the type of response that they can measure may hinder an effective monitoring of the structural state; this can be overcome through redundancy and an efficient sensor placement. In this article, an automated approach is discussed for obtaining the optimal topology of a sparse MEMS sensor network. In this regard, the scenarios are assumed unknown in terms of extent and location of stiffness degradation due to damage. The optimal sensor locations are obtained via maximization of the global sensitivity of the measurements to damage. The method can be also implemented in a multi-scale framework, to efficiently handle (micro) sensors, (meso) damaged regions and (macro) structural components of (by far) different sizes. Data related to composite specimens and panels are discussed, with the aim of assessing the identifiability of damage through self-adapting theoretical/numerical (digital) twins of the monitored structures.","PeriodicalId":296832,"journal":{"name":"2020 5th International Conference on Robotics and Automation Engineering (ICRAE)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132560285","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