کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6883633 | 1444177 | 2017 | 10 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Cloud robotics in Smart Manufacturing Environments: Challenges and countermeasures
ترجمه فارسی عنوان
ابر روباتیک در محیط های هوشمند ساخت و ساز: چالش ها و اقدامات مقابله با آن
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کلمات کلیدی
ابر روباتیک، تولید هوشمند، محلی سازی و نقشه برداری همزمان، پردازش ابری،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
شبکه های کامپیوتری و ارتباطات
چکیده انگلیسی
In Smart Manufacturing Environments (SME), the use of cloud robotics is based on the integration of cloud computing and industrial robots, which provides a new technological approach to task execution and resource sharing compared to traditional industrial robots. However, research on cloud robotics in SME still faces some challenges. First, highly flexible load scheduling mechanisms are immature. Second, traditional optimization mechanisms for the network service quality do not meet the requirements of smart manufacturing due to time variability and service quality dynamics. And, finally, existing learning algorithms used without cloud-assisted resources cause great resource wasting. Accordingly, this paper explores main technologies related to cloud robotics in SME. The research contents include self-adaptive adjustment mechanisms for the service quality of a cloud robot network, computing load allocation mechanisms for cloud robotics, and group learning based on a cloud platform. The results presented in this paper are helpful to understand the internal mechanisms of perception and interaction, intelligent scheduling and control of cloud robot systems oriented to smart manufacturing, and the design of a cloud architecture oriented to group learning.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computers & Electrical Engineering - Volume 63, October 2017, Pages 56-65
Journal: Computers & Electrical Engineering - Volume 63, October 2017, Pages 56-65
نویسندگان
Hehua Yan, Qingsong Hua, Yingying Wang, Wenguo Wei, Muhammad Imran,