On-line Learning for Robotic Assembly Using Artificial Neural Networks and Contact Force Sensing
By: Ismael Lopez-Juarez
Series: PhD thesis
Publisher: The Nottingham Trent University
Published: 2000
Place of publication: Nottngham, UK
Language: eng
Format: A4
Number of pages: 266
Copyright date: 2000
Description: Traditionally, robotic assembly techniques have depended on simple sensing systems and the robot manufacturer’s programming language, which has severely
restricted the extensive use of robots in complex manufacturing operations. The research reported in this thesis is related to the creation of self-adapting robots
capable of learning manipulative skills on-line. This work involves the use of Artificial Neural Networks (ANNs) and contact force sensing to “teach” the robot
how to behave in poorly structured environments.
An industrial PUMA 761 robot arm was provided for this research by Rolls Royce & Associates, who are interested in autonomous robot operations. The
investigation includes the design of a novel Neural Network Controller (NNC), which is based on the Adaptive Resonance Theory (ART) and a knowledge base,
whose knowledge is generated by specific assembly operations.
The research used a force/torque sensor attached to the robot’s wrist. This was the only sensory information available to the NNC during assembly operations
since the precise location of the components was unknown. The communication with the robot controller was made through a PC master-slave architecture, which
provided data acquisition and control in real-time.