Gilbreth 2.0: An Industrial Cloud Robotics Pick-and-Sort Application

2019 ◽  
Author(s):  
Yizhe Zhang ◽  
Lianjun Li ◽  
Jorge Nicho ◽  
Michael Ripperger ◽  
Andrea Fumagalli ◽  
...  
Keyword(s):  
Cloud Robotics ◽  
Industrial Cloud

Knowledge Sharing and Evolution of Industrial Cloud Robotics

2018 ◽  
Author(s):  
Lixue Jin ◽  
Wenjun Xu ◽  
Zhihao Liu ◽  
Junwei Yan ◽  
Zude Zhou ◽  
...  
Keyword(s):  
Knowledge Sharing ◽  
Resource Sharing ◽  
Industrial Robot ◽  
Cloud Service ◽  
Semantic Knowledge ◽  
Industrial Robots ◽  
Online Data ◽  
Knowledge Evolution ◽  
Cloud Robotics ◽  
Industrial Cloud

Industrial Cloud Robotics (ICR), with the characteristics of resource sharing, lower cost and convenient access, etc., can realize the knowledge interaction and coordination among cloud Robotics (CR) through the knowledge sharing mechanism. However, the current researches mainly focus on the knowledge sharing of service-oriented robots and the knowledge updating of a single robot. The interaction and collaboration among robots in a cloud environment still have challenges, such as the improper updating of knowledge, the inconvenience of online data processing and the inflexibility of sharing mechanism. In addition, the industrial robot (IR) also lacks a well-developed knowledge management framework in order to facilitate the knowledge evolution of industrial robots. In this paper, a knowledge evolution mechanism of ICR based on the approach of knowledge acquisition - interactive sharing - iterative updating is established, and a novel architecture of ICR knowledge sharing is also developed. Moreover, the semantic knowledge in the robot system can encapsulate knowledge of manufacturing tasks, robot model and scheme decision into the cloud manufacturing process. As new manufacturing tasks arrived, the robot platform downloads task-oriented knowledge models from the cloud service platform, and then selects the optimal service composition and updates the cloud knowledge by simulation iterations. Finally, the feasibility and effectiveness of the proposed architecture and approaches are demonstrated through the case studies.

Digital Twin-Based Control Approach for Industrial Cloud Robotics

2019 ◽  
Author(s):  
Lan Li ◽  
Wenjun Xu ◽  
Zhihao Liu ◽  
Bitao Yao ◽  
Zude Zhou ◽  
...  
Keyword(s):  
Intelligent Agents ◽  
Effective Means ◽  
Physical World ◽  
Cloud Services ◽  
Industrial Robots ◽  
Control Approach ◽  
Digital Twin ◽  
Digital World ◽  
Cloud Robotics ◽  
Industrial Cloud

Abstract Industrial robots can be mechanical intelligent agents by integrating programs, intelligent algorithms and facilitating intelligent manufacturing models from cyber world into physical entities. After introducing the concept of cloud, their storage, computing, knowledge sharing and evolution capabilities are further strengthened. Digital twin is an effective means to achieve the fusion of physics and information. Therefore, it is feasible to introduce the digital twin to the industrial cloud robotics (ICR), in order to facilitate the control optimization of robots’ running state. The traditional manufacturing task-oriented service composition is limited to execution in the cloud, and it is separated from the underlying robot equipment control, which greatly reduces the real-time performance and accuracy of the underlying service response, such as Robotic Control as a Cloud Service (RCaaCS). Therefore, this paper proposes a digital twin-based control approach for ICR. At the manufacturing cell level, robots’ control instruction service modeling is conducted, and then the control service in the digital world is mapped to the robot action control in the physical world through the concept of digital twin. The accumulated operational data in the physical world can be fed back to the digital world as a reference for simulation and control strategy adjustment, finally achieving the integration of cloud services and robot control. A case study based on workpiece disassembly is presented to verify the availability and effectiveness of the proposed control approach.

Industrial Cloud Robotics Towards Sustainable Manufacturing

2016 ◽  
Author(s):  
Jiayi Liu ◽  
Wenjun Xu ◽  
Jiaqiang Zhang ◽  
Zude Zhou ◽  
Duc Truong Pham
Keyword(s):  
Energy Consumption ◽  
Sustainable Manufacturing ◽  
Industrial Robots ◽  
Production Costs ◽  
Pollutant Emissions ◽  
Actual Problem ◽  
Cloud Robotics ◽  
Stage Of Development ◽  
Energy Consumption Optimization ◽  
Industrial Cloud

Cloud Robotics (CR) is the combination of Cloud Computing and Robotics, which encapsulate resources related with robots as services and is also the robotics’ next stage of development. Under this background, due to the characteristics of convenient access, resource sharing and lower costs, industrial cloud robotics (ICR) is proposed to integrate the industrial robots resources in the worldwide to provide ICR services in worldwide. ICR also plays an important role in improving the productivity of manufacturing. In the manufacturing field, Cloud Manufacturing (CM) and Sustainable Manufacturing (SM) is the developing orientation of future manufacturing industry. The energy consumption optimization of ICR is the crucial issue for manufacturing sustainability. However, currently, ICR systems are not programmed efficiently, which leads to the increase of production costs and pollutant emissions. Thus, it is an actual problem to optimize the energy consumption of ICR. In this paper, in order to achieve the goal of energy consumption optimization in worldwide range, the framework of ICR towards sustainable manufacturing is presented, as well as its enabling methodologies, and it is used to support energy consumption optimization services of ICR in the Cloud environment. This framework can be used to support energy-efficient services related with ICR to realize sustainable manufacturing in the worldwide range.

scholarly journals Dynamic Assessment of Sustainable Manufacturing Capability Based on Correlation Relationship for Industrial Cloud Robotics

2021 ◽  
Author(s):  
Sisi Tian ◽  
Xiaotong Xie ◽  
Wenjun Xu ◽  
Jiayi Liu ◽  
Xiaomei Zhang
Keyword(s):  
Dynamic Assessment ◽  
Sustainable Manufacturing ◽  
Indicator System ◽  
Assessment Model ◽  
Correlation Model ◽  
Correlation Relationship ◽  
Cloud Robotics ◽  
Manufacturing Capability ◽  
Service Correlation ◽  
Industrial Cloud

Abstract The industrial cloud robotics (ICRs) integrates distributed industrial robot resources in various places to support complex task processing for multi-resource service requirements, and manufacturing capability assessment is the key link in determining the optimal service composition to realize the value-added of ICRs resources. However, the traditional evaluation method ignores the positive and negative cooperative effects of the manufacturing capability correlation among the robot individuals on the overall manufacturing capability of the ICRs composition. In addition, the problems of excessive resource consumption and serious environmental pollution in the manufacturing industry are becoming increasingly serious. The paper proposes a dynamic assessment method of sustainable manufacturing capability for ICRs based on the correlation relationship to solve above problems. Firstly, an extensible multi-dimensional indicator system of sustainable manufacturing capability is constructed. Then, multiple composition correlation relationships among ICRs are analyzed to establish the correlation assessment model. Furthermore, a set of dynamic evaluation methods is proposed, in which the evaluation indicators raw data is processed based on the service correlation model and the traditional network analytic network process method is improved based on the data correlation model. Finally, a case study is implemented to show the reasonability and effectiveness of the proposed method in assessment of sustainable manufacturing capability for ICRs.

scholarly journals Energy Condition Perception and Big Data Analysis for Industrial Cloud Robotics

Procedia CIRP ◽  
2017 ◽  
Vol 61 ◽  
pp. 370-375 ◽  
Author(s):  
Wei Xu ◽  
Quan Liu ◽  
Wenjun Xu ◽  
Zude Zhou ◽  
Duc Truong Pham ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Energy Condition ◽  
Big Data Analysis ◽  
Cloud Robotics ◽  
Industrial Cloud

Knowledge Graph-Based Manufacturing Capability Service Optimal Selection for Industrial Cloud Robotics

2020 ◽  
Author(s):  
Yanping Ma ◽  
Wenjun Xu ◽  
Sisi Tian ◽  
Jiayi Liu ◽  
Bitao Yao ◽  
...  
Keyword(s):  
Basic Function ◽  
Industrial Robots ◽  
Robotic Assembly ◽  
Knowledge Graph ◽  
Optimal Selection ◽  
Cloud Robotics ◽  
Manufacturing Capability ◽  
Selection For ◽  
Function Information ◽  
Industrial Cloud

Abstract As an important part of Cloud Manufacturing (CMfg), Industrial Cloud Robotics (ICRs) encapsulates manufacturing capability of physical industrial robots as services for the users. However, a growing number of functionally equivalent services appear in CMfg platform due to the wide use of industrial robots in manufacturing field. It is important to carry out Manufacturing Capability Service (MCS) optimal selection for ICRs from various optional services under CMfg environment. But current service optimal selection method emphasizes on the non-function information of services, and it ignores the interactive relationships between different services and the basic function information of services, which make it difficult to satisfy the various personalized demands of users. Service optimal selection requires the integration and sharing of manufacturing knowledge. Knowledge graph provides an effective way to express and manage knowledge. And it can provide decision support for users to select appropriate ICRs service. Therefore, this paper proposes a method of knowledge graph-based manufacturing capability service optimal selection for ICRs. The function information, association information and non-function information of MCS are described based on knowledge graph. Based on this, the service optimal selection procedure is proposed to realize smart MCS optimal selection for ICRs, which includes feature selection, association selection and user custom weights of non-function indices selection. Finally, a case study based on robotic assembly is presented to demonstrate the effectiveness of proposed method.

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