Planning and Control of a Robotic Manipulator Using Neural Networks

Xavier J. R. Avula, Missouri University of Science and Technology
Heng Ma
Anil Malkani
Jay-Shinn Tsai
Luis C. Rabelo

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An architecture which utilizes two artificial neural systems for planning and control of a robotic arm is presented. The first neural network system participates in the trajectory planning and the motion decision-making process. The second neural network system provides the correct sequence of control actions with a high accuracy due to the utilization of an unsupervised/supervised neural network scheme. The utilization of a hybrid hierarchical/distributed organization, supervised/unsupervised learning models, and forward modeling yielded an architecture with capabilities of high level functionality.