
Artificial neural network based robot control: An overview
The current thrust of research in robotics is to build robots which can operate in dynamic and/or partially known environments. The ability of learning endows the robot with a form of autonomous intelligence to handle such situations. This paper focuses on the intersection of the fields of robot control and learning methods as represented by artificial neural networks. An in-depth overview of the application of neural networks to the problem of robot control is presented. Some typical neural network architectures are discussed first. The important issues involved in the study of robotics are then highlighted. This paper concentrates on the neural network applications to the motion control of robots involved in both non-contact and contact tasks. The current state of research in this area is surveyed and the strengths and weakness of the present approaches are emphasized. The paper concludes by identifying areas which need future research work.
Duke Scholars
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Related Subject Headings
- Industrial Engineering & Automation
- 4602 Artificial intelligence
- 4007 Control engineering, mechatronics and robotics
- 1702 Cognitive Sciences
- 0801 Artificial Intelligence and Image Processing
Citation

Published In
DOI
ISSN
Publication Date
Volume
Issue
Start / End Page
Related Subject Headings
- Industrial Engineering & Automation
- 4602 Artificial intelligence
- 4007 Control engineering, mechatronics and robotics
- 1702 Cognitive Sciences
- 0801 Artificial Intelligence and Image Processing