Pushing operation of manipulator based on experience learning: Position prediction of an object and pushing analysis

Author

Haolin Yang ; Fuchun Sun ; Di Guo

Author_Institution

Dept. of Comput. Sci. & Technol., Tsinghua Univ., Beijing, China

fYear

2014

fDate

28-29 Sept. 2014

Firstpage

1

Lastpage

6

Abstract

With the popularization and application of manipulator in the field of industrial production and scientific experiment, it is crucial to focus on the manipulator motion control. Pushing is the basis and assistance of complex operations of manipulator, and research goes on pushing operation has a very important and practical significance. Learning from experience has been recognized as an important cognitive ability in the cognitive architecture, and it makes the manipulator repeat the same or similar task in similar environments. The goal of this paper is to achieve the position prediction of a target object in pushing operation with experience learning and analyze the pushing operation.

Keywords

image colour analysis; industrial manipulators; learning (artificial intelligence); motion control; robot vision; experience learning; industrial production; manipulator motion control; manipulator pushing operation; object position prediction; pushing analysis; Acceleration; Force; Image resolution; Least squares methods; Manipulators; Neural networks; Training;

fLanguage

English

Publisher

ieee

Conference_Titel

Multisensor Fusion and Information Integration for Intelligent Systems (MFI), 2014 International Conference on

Conference_Location

Beijing

Print_ISBN

978-1-4799-6731-5

Type

conf

DOI

10.1109/MFI.2014.6997642

Filename

6997642