کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6868141 | 680747 | 2016 | 9 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Adaptive control algorithm of flexible robotic gripper by extreme learning machine
ترجمه فارسی عنوان
الگوریتم کنترل انطباق از دستکاری رباتیک انعطاف پذیر توسط دستگاه یادگیری افراطی
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کلمات کلیدی
گیره انعطاف پذیر، سنسورها، تشخیص شی، محاسبات نرم،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
Adaptive grippers should be able to detect and recognize grasping objects. To be able to do it control algorithm need to be established to control gripper tasks. Since the gripper movements are highly nonlinear systems it is desirable to avoid using of conventional control strategies for robotic manipulators. Instead of the conventional control strategies more advances algorithms can be used. In this study several soft computing methods are analyzed for robotic gripper applications. The gripper structure is fully compliant with embedded sensors. The sensors could be used for grasping shape detection. As soft computing methods, extreme learning machine (ELM) and support vector regression (SVR) were established. Also other soft computing methods are analyzed like fuzzy, neuro-fuzzy and artificial neural network approach. The results show the highest accuracy with ELM approach than other soft computing methods.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Robotics and Computer-Integrated Manufacturing - Volume 37, February 2016, Pages 170-178
Journal: Robotics and Computer-Integrated Manufacturing - Volume 37, February 2016, Pages 170-178
نویسندگان
Dalibor PetkoviÄ, Amir Seyed Danesh, Mehdi Dadkhah, Negin Misaghian, Shahaboddin Shamshirband, Erfan Zalnezhad, Nenad D. PavloviÄ,