Cloud Robotics Towards a CPS Assembly System

2017 ◽  
pp. 243-259 ◽  
Author(s):  
Lihui Wang ◽  
Xi Vincent Wang
Keyword(s):  
Assembly System ◽  
Cloud Robotics

Automatic Assembly System of Strain Gauge Based on Deviation Model

ROBOT ◽  
2013 ◽  
Vol 35 (5) ◽  
pp. 589
Author(s):  
Dechun ZHENG ◽  
Yongping ZHANG ◽  
Guojun LI
Keyword(s):  
Strain Gauge ◽  
Assembly System ◽  
Automatic Assembly

Pose algorithm and assembly system development on complex joints of steel structure

2015 ◽  
Author(s):  
J.J. Shou ◽  
Z.P. Chen ◽  
Q.Q. Zhou ◽  
Z.Y. Zhou ◽  
S. Mou ◽  
...  
Keyword(s):  
System Development ◽  
Steel Structure ◽  
Assembly System

Open CLORO: An Open Testbed for Cloud Robotics

2019 ◽  
Author(s):  
Giuseppe Portaluri ◽  
Mike Ojo ◽  
Stefano Giordano ◽  
Marialaura Tamburello ◽  
Giuseppe Caruso
Keyword(s):  
Cloud Robotics

Study of self-assembly system of norfloxacin molecularly imprinted polymers based on simulated design

2021 ◽  
Vol 140 (1) ◽  
Author(s):  
Xiaoshuang Wang ◽  
Wensi Zhao ◽  
Junbo Liu ◽  
Shanshan Tang ◽  
Ruifa Jin
Keyword(s):  
Molecularly Imprinted Polymers ◽  
Self Assembly ◽  
Assembly System ◽  
Molecularly Imprinted ◽  
Imprinted Polymers

scholarly journals Networking for Cloud Robotics: The DewROS Platform and Its Application

2021 ◽  
Vol 10 (2) ◽  
pp. 34
Author(s):  
Alessio Botta ◽  
Jonathan Cacace ◽  
Riccardo De Vivo ◽  
Bruno Siciliano ◽  
Giorgio Ventre
Keyword(s):  
Response Time ◽  
Large Data ◽  
Cloud Services ◽  
Cloud Provider ◽  
Human Beings ◽  
Minimal Impact ◽  
Network Connection ◽  
Cloud Robotics ◽  
Networking Technologies ◽  
The Impact

With the advances in networking technologies, robots can use the almost unlimited resources of large data centers, overcoming the severe limitations imposed by onboard resources: this is the vision of Cloud Robotics. In this context, we present DewROS, a framework based on the Robot Operating System (ROS) which embodies the three-layer, Dew-Robotics architecture, where computation and storage can be distributed among the robot, the network devices close to it, and the Cloud. After presenting the design and implementation of DewROS, we show its application in a real use-case called SHERPA, which foresees a mixed ground and aerial robotic platform for search and rescue in an alpine environment. We used DewROS to analyze the video acquired by the drones in the Cloud and quickly spot signs of human beings in danger. We perform a wide experimental evaluation using different network technologies and Cloud services from Google and Amazon. We evaluated the impact of several variables on the performance of the system. Our results show that, for example, the video length has a minimal impact on the response time with respect to the video size. In addition, we show that the response time depends on the Round Trip Time (RTT) of the network connection when the video is already loaded into the Cloud provider side. Finally, we present a model of the annotation time that considers the RTT of the connection used to reach the Cloud, discussing results and insights into how to improve current Cloud Robotics applications.

scholarly journals Exploring the application of machine learning to the assembly line feeding problem

2021 ◽  
Author(s):  
Moretti Emilio ◽  
Tappia Elena ◽  
Limère Veronique ◽  
Melacini Marco
Keyword(s):  
Machine Learning ◽  
Assembly Line ◽  
Optimal Solution ◽  
Product Variety ◽  
Supervised Machine Learning ◽  
Assembly System ◽  
Optimization Approach ◽  
Feeding Problem ◽  
Managerial Implications ◽  
Multiple Feeding

AbstractAs a large number of companies are resorting to increased product variety and customization, a growing attention is being put on the design and management of part feeding systems. Recent works have proved the effectiveness of hybrid feeding policies, which consist in using multiple feeding policies in the same assembly system. In this context, the assembly line feeding problem (ALFP) refers to the selection of a suitable feeding policy for each part. In literature, the ALFP is addressed either by developing optimization models or by categorizing the parts and assigning these categories to policies based on some characteristics of both the parts and the assembly system. This paper presents a new approach for selecting a suitable feeding policy for each part, based on supervised machine learning. The developed approach is applied to an industrial case and its performance is compared with the one resulting from an optimization approach. The application to the industrial case allows deepening the existing trade-off between efficiency (i.e., amount of data to be collected and dedicated resources) and quality of the ALFP solution (i.e., closeness to the optimal solution), discussing the managerial implications of different ALFP solution approaches and showing the potential value stemming from machine learning application.

Triple-Modulated Chiral Inversion of Co-Assembly System Based on Alanine Amphiphile and Cyanostilbene Derivative

2021 ◽  
Author(s):  
Penghua Qin ◽  
Zhen Wu ◽  
Pengyu Li ◽  
Dian Niu ◽  
Minghua Liu ◽  
...  
Keyword(s):  
Assembly System ◽  
Chiral Inversion

scholarly journals Assembly system design in captive offshoring – A semi-systematic review of literature

Procedia CIRP ◽  
2021 ◽  
Vol 97 ◽  
pp. 313-318
Author(s):  
Peter Burggräf ◽  
Matthias Dannapfel ◽  
Tobias Adlon ◽  
Katharina Müller
Keyword(s):  
Systematic Review ◽  
System Design ◽  
Assembly System ◽  
Review Of Literature ◽  
Assembly System Design ◽  
Captive Offshoring
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