• Complex
  • Title
  • Keyword
  • Abstract
  • Scholars
  • Journal
  • ISSN
  • Conference
搜索

Author:

Yu, Jianjun (Yu, Jianjun.) | Wu, Pengshen (Wu, Pengshen.) | Yu, Naigong (Yu, Naigong.) (Scholars:于乃功) | Zuo, Guoyu (Zuo, Guoyu.) (Scholars:左国玉) | Zhang, Yuan (Zhang, Yuan.)

Indexed by:

EI Scopus

Abstract:

In order to simplify the complex motion planning and improve the intelligence of robot arm, a robot arm task imitation system based on RNN (Recurrent Neural Network) is proposed. Firstly, the original task is demonstrated to robot arm, and the original data is collected which includes original task trajectory data and robot arm joint angle data. Secondly, RNN is constructed and used to obtain imitation policy by training original data. Thirdly, when task changes, new data is collected which only include new task trajectory data, and robot arm joint angle data is obtained by imitation policy generalization of new data. The experimental results show that the imitation system not only can simplify complex motion planning and reproduce demonstration of original task, but also can realize new task imitation by policy generalization when task changes. © 2017 IEEE.

Keyword:

Biomimetics Recurrent neural networks Robotic arms Agricultural robots Complex networks Robotics Intelligent robots Robot programming Motion planning

Author Community:

  • [ 1 ] [Yu, Jianjun]Faculty of Information Technology, Beijing University of Technology, Beijing, China
  • [ 2 ] [Wu, Pengshen]Faculty of Information Technology, Beijing University of Technology, Beijing, China
  • [ 3 ] [Yu, Naigong]Faculty of Information Technology, Beijing University of Technology, Beijing, China
  • [ 4 ] [Zuo, Guoyu]Faculty of Information Technology, Beijing University of Technology, Beijing, China
  • [ 5 ] [Zhang, Yuan]Faculty of Information Technology, Beijing University of Technology, Beijing, China

Reprint Author's Address:

  • [yu, jianjun]faculty of information technology, beijing university of technology, beijing, china

Show more details

Related Keywords:

Related Article:

Source :

Year: 2017

Volume: 2018-January

Page: 2484-2489

Language: English

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 2

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 9

Online/Total:732/10551856
Address:BJUT Library(100 Pingleyuan,Chaoyang District,Beijing 100124, China Post Code:100124) Contact Us:010-67392185
Copyright:BJUT Library Technical Support:Beijing Aegean Software Co., Ltd.