Title :
Neural network architectures for robotic applications
Author :
King, S.-Y. ; Hwang, Jenq-Neng
Author_Institution :
Dept. of Electr. Eng., Princeton Univ., NJ, USA
fDate :
10/1/1989 12:00:00 AM
Abstract :
The authors propose a ring VLSI systolic architecture for implementing artificial neural networks (ANNs) with applications to robotic processing. Key design issues concerning algorithms, applications, and architectures are examined. A variety of neural networks is considered, including single-layer feedback neural networks, competitive learning networks, and multilayer feed-forward networks. It is demonstrated that the ANNs are suitable to all three levels of robotic processing applications including task planning, path planning, and path control levels. For these applications, a programmable systolic array is developed than can exploit the strength of VLSI to provide intensive and pipelined computing. Both the retrieving and learning phases are integrated in the design. The proposed architecture, which is more versatile than other existing ANNs, can accommodate all the useful neural networks for robotic processing
Keywords :
VLSI; artificial intelligence; neural nets; parallel architectures; pipeline processing; robots; artificial intelligence; competitive learning networks; multilayer feed-forward networks; neural networks; path control; path planning; pipeline processing; ring VLSI; robotic processing; systolic architecture; task planning; Algorithm design and analysis; Artificial neural networks; Feedforward neural networks; Multi-layer neural network; Neural networks; Neurofeedback; Path planning; Process design; Robots; Very large scale integration;
Journal_Title :
Robotics and Automation, IEEE Transactions on