Cloud robotics in Smart Manufacturing Environments: Challenges and countermeasures

Yan, Hehua; Hua, Qingsong; Wang, Yingying; Wei, Wenguo; Imran, Muhammad

doi:10.1016/j.compeleceng.2017.05.024

Cited by 53 publications

(34 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With cloud computing, task executions are established invoking cloud technologies, assuring integration and interoperability [ 68 ]. In manufacturing, the assembly process has also changed in the Industry 4.0 era, evolving into the robotic assembly line, where robots manage to perform tasks previously performed by human labor [ 69 ].…”

Section: Smart Domains and Applications In The Iort Systemsmentioning

confidence: 99%

Internet of Robotic Things in Smart Domains: Applications and Challenges

Romeo

Petitti

Marani

et al. 2020

Sensors

110

View full text Add to dashboard Cite

With the advent of the Fourth Industrial Revolution, Internet of Things (IoT) and robotic systems are closely cooperating, reshaping their relations and managing to develop new-generation devices. Such disruptive technology corresponds to the backbone of the so-called Industry 4.0. The integration of robotic agents and IoT leads to the concept of the Internet of Robotic Things, in which innovation in digital systems is drawing new possibilities in both industrial and research fields, covering several domains such as manufacturing, agriculture, health, surveillance, and education, to name but a few. In this manuscript, the state-of-the-art of IoRT applications is outlined, aiming to mark their impact on several research fields, and focusing on the main open challenges of the integration of robotic technologies into smart spaces. IoRT technologies and applications are also discussed to underline their influence in everyday life, inducing the need for more research into remote and automated applications.

show abstract

Section: Smart Domains and Applications In The Iort Systemsmentioning

confidence: 99%

Internet of Robotic Things in Smart Domains: Applications and Challenges

Romeo

Petitti

Marani

et al. 2020

Sensors

110

View full text Add to dashboard Cite

show abstract

“…Activities are synchronized via an online cloud database and, therefore, remote access to operation is possible via the internet, but analyzing, controlling and storing data is also possible. According to [26], cloud technologies, such as CC and Cloud Storage (CS), can be very beneficial for robotics. The robot nodes can access knowledge by communicating with the cloud, effectively overcoming the knowledge and learning limitations on the robot.…”

Section: Materials Handling Equipmentmentioning

confidence: 99%

Cloud and IoT Applications in Material Handling Automation and Intralogistics

Ponis

Efthymiou

2020

Logistics

View full text Add to dashboard Cite

During the last decade, digitalization has borne tremendous changes on the way we live and do business. Industry 4.0, the new industrial revolution, is merging the physical, digital and virtual worlds through emerging technologies that collide with each other and create a distinctive paradigm shift. Even though the topic of Industry 4.0, has attracted significant attention during the past few years, literature in this subject area is still limited. The main objective of this paper is to study the current state of the art and identify major trends and research shortcomings. To that end, the authors conducted a methodological literature review based primarily on the SCOPUS bibliographic database. The review returned 49 relative papers dealing with the paper’s subject area. Through a thorough study of the selected papers, four dominant literature categories were recognized and discussed in detail. According to the literature reviewed, it is evident that massive changes are underway for warehouses and intralogistics facilities. Still, despite the intense discussion and appeal of the subject, one of the most important challenges in the scientific area under study, as the literature highlights, is the absence of a matching, to its significance, number of real-life applications. To that end, this paper provides a detailed description of a Cloud-based IoT application drawn from a Distribution Center (DC) that supplies retail home furnishing and sporting goods products to stores in Greece and the Balkan region, with the objective to showcase the feasibility of such an investment, highlight its potential and provide motivation to practitioners to evaluate and proceed in similar technological investments.

show abstract

“…M. Lang et al [26] identified service function, legality, contract, geographic location, and flexibility as the highest quality-ofservice criteria for cloud service selection through Delphi research on cloud service quality attributes. H. Yan et al [27] studied the related technologies of cloud robots in intelligent manufacturing environment, including the adaptive adjustment mechanism of cloud robot network service quality, the computing load distribution mechanism of cloud robots, and the group learning based on cloud platform. J. Zhou et al [28] improved the artificial bee colony algorithm by introducing food source disturbance synergy mechanism, diversity maintenance strategy, and new computing resource allocation scheme to deal with multiobjective service composition and optimal selection problems in complex cloud manufacturing.…”

Section: Literature Reviewmentioning

confidence: 99%

Cloud Manufacturing Service Composition Optimization with Improved Genetic Algorithm

Yao

Liu

2019

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Aiming at the problems in which there exists collocation between services and manufacturing tasks, multiobjective cloud manufacturing service composition optimization seldom considers the synergy degree of composite cloud services and the complexity of service composition, so a novel service composition optimization approach, called improved genetic algorithm based on entropy (IGABE), is put forward. First, the mathematical expressions of service collocation degree, composition synergy degree, composition entropy, and their related influence factors of the service composition are analyzed, and their definitions and calculation methods are given. Then, a multiobjective cloud manufacturing service composition optimization mathematical model is established. Moreover, crossover and mutation operators are improved by introducing normal cloud model theory and piecewise function, and improved roulette selection method is used to perform the selection operation. And the fitness function of the proposed IGABE is designed by combining Euclidean deviation with angular deviation. Finally, the manufacturing task of a wheeled cleaning robot is exemplified to verify the correctness of the proposed multiobjective optimization model for cloud manufacturing service composition and the effectiveness of the proposed algorithm, compared with Standard Genetic Algorithm (SGA), Hybrid Genetic Algorithm (HGA), and Cloud-entropy Enhanced Genetic Algorithm (CEGA). The studied results show that IGABE converges faster than SGA and HGA and can analyze and reflect the content difference expressed by the objective functions of service composition scheme and its approximation degree to the corresponding dimensions of the ideal point vector more comprehensively than CEGA. As such, the optimal service composition obtained by IGABE algorithm can better meet the complex needs of users.

show abstract

Cloud robotics in Smart Manufacturing Environments: Challenges and countermeasures

Cited by 53 publications

References 12 publications

Internet of Robotic Things in Smart Domains: Applications and Challenges

Internet of Robotic Things in Smart Domains: Applications and Challenges

Cloud and IoT Applications in Material Handling Automation and Intralogistics

Cloud Manufacturing Service Composition Optimization with Improved Genetic Algorithm

Contact Info

Product

Resources

About