شبیه سازی دینامیک های معکوس ربات
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شبیه سازی دینامیک های معکوس ربات

عنوان فارسی مقاله: مدل سازی و شبیه سازی دینامیک های معکوس ربات با استفاده از الگوریتم یادگیری عمیق مبتنی بر LSTM (حافظه کوتاه مدت طولانی) برای شهرها و کارخانه های هوشمند
عنوان انگلیسی مقاله: Modeling and Simulation of Robot Inverse Dynamics Using LSTM-Based Deep Learning Algorithm for Smart Cities and Factories
مجله/کنفرانس: دسترسی – IEEE Access
رشته های تحصیلی مرتبط: مهندسی کامپیوتر، مهندسی برق
گرایش های تحصیلی مرتبط: هوش مصنوعی، رباتیک
کلمات کلیدی فارسی: شهرها و کارخانه های هوشمند، دینامیک های معکوس، ربات، رایانش سبز، یادگیری عمیق، حافظه کوتاه مدت طولانی
کلمات کلیدی انگلیسی: Smart cities and factories, inverse dynamics, robot, green computing, deep learning, LSTM
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus – Master Journals List – JCR
شناسه دیجیتال (DOI): https://doi.org/10.1109/ACCESS.2019.2957019
دانشگاه: School of Mechanical Engineering, Tianjin Polytechnic University, Tianjin 300387, China
ناشر: آی تریپل ای - IEEE
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2019
ایمپکت فاکتور: 4.641 در سال 2018
شاخص H_index: 56 در سال 2019
شاخص SJR: 0.609 در سال 2018
شناسه ISSN: 2169-3536
شاخص Quartile (چارک): Q2 در سال 2018
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 10
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: ندارد
کد محصول: E14074
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست انگلیسی مطالب

ABSTRACT


I. INTRODUCTION


II. PROPOSED SCHEME


III. PERFORMANCE EVALUATION


IV. CONCLUSION


REFERENCES

نمونه متن انگلیسی مقاله

ABSTRACT


In smart cities and factories, robotic applications require high dexterity and security, which requires precise inverse dynamics model. However, the physical modeling methods cannot model the uncertain factors of the manipulator such as flexibility, joint clearance and friction, etc. As an alternative, artificial intelligence (AI) techniques have become increasingly popular in robotics for smart cities and factories. In this paper, deep learning neural network based on LSTM (Long Short-Term Memory) is adopted to predict the manipulator inverse dynamics. This study aims to summarize the influence of the hyper-parameter settings on model performance and to explore the applicability of the LSTM model to joint torque prediction of multiple degrees of freedom series manipulator. Furthermore, the feasibility of using only joint position as input data for torque prediction is verified. Simulation result has shown that, for the proposed deep learning architecture, the effects of the number of maximum epochs on model performance should be prioritized. The effects of the number of hidden nodes on model performance are limited, while prediction accuracy will deteriorate as the number of hidden layers increases. It is proved that it is feasible to predict inverse dynamics when input data is joint position only. The experimental results show that the training time increases with the increase of hidden layers, neurons and epochs.


INTRODUCTION


Smart city is an intelligent city based on internet of things, cloud computing and artificial intelligence technology. It adopts advanced information technology, analyzes the trend of the city, makes quick intelligent responses to urban planning, livelihood policies, social security and other aspects, and realizes the intelligent management of the city. At present, there are many urban problems, such as air pollution, water pollution, garbage pollution, shortage of resources, traffic jam and so on. These problems seriously affect people’s life and hinder the development of the city. To solve these problems, it is necessary to build smart cities to improve people’s way of life, create a beautiful life and environment, and promote urban development and innovation. Similar to smart city, smart factory is composed of many intelligent manufacturing equipments (including control and information systems), namely several intelligent branches and equipment that is composed of various intelligent components. Robots used for smart cities and factories have accomplished some easy tasks in structured settings that still require fences between the robots and human to ensure safety. Ideally, robots should be able to work side by side with humans, offering their strength to carry heavy loads while presenting no danger. To achieve this objective, it is necessary to obtain an accurate inverse dynamics model of the robot. Moreover, inverse dynamics has been a valuable piece of information for robotic function such as compliance control, human-robot cooperation, target operation and trajectory planning.

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