Two artificial neural network systems are considered in a hierarchical fashion to plan the trajectory and control of a robotic arm. At the higher level of the hierarchy the neural system consists of four networks: a restricted Coulomb energy network to delineate the robot arm workspace; two standard backpropagation (BP) networks for coordinates transformation; and a fourth network which also uses BP and participates in the trajectory planning by cooperating with other knowledge sources. The control emulation process which is developed using a second neural system at a lower hierarchical level provides the correct sequence of control actions. An example is presented to illustrate the capabilities of the developed architectures.
X. J. Avula and L. C. Rabelo, "Intelligent Control of a Robotic Arm Using Hierarchical Neural Network Systems," Proceedings of the Seattle International Joint Conference on Neural Networks, 1991, Institute of Electrical and Electronics Engineers (IEEE), Jan 1991.
The definitive version is available at https://doi.org/10.1109/IJCNN.1991.155428
Seattle International Joint Conference on Neural Networks, 1991
Chemical and Biochemical Engineering
Keywords and Phrases
Backpropagation; Control Emulation Process; Hierarchical Neural Network; Intelligent Control; Knowledge Sources; Learning Systems; Neural Nets; Planning (Artificial Intelligence); Position Control; Restricted Coulomb Energy Network; Robotic Arm; Robots; Trajectory Planning
Article - Conference proceedings
© 1991 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.