Open Access. Powered by Scholars. Published by Universities.®
- Discipline
Articles 1 - 3 of 3
Full-Text Articles in Engineering
An Improved Method For Online Calculation And Compensation Of The Static Deflection At A Robot End-Effector, Paul P. Lin, Hsiang-Dih Chiang, Xiu Xun Cui
An Improved Method For Online Calculation And Compensation Of The Static Deflection At A Robot End-Effector, Paul P. Lin, Hsiang-Dih Chiang, Xiu Xun Cui
Mechanical Engineering Faculty Publications
Traditionally, robotic deflection analysis for a low-weight robot has been performed based on an assumption that each link is treated as a cantilever beam, which leads to no angular deflection at a joint. In practice, a robotic intermediate joint is linearly and angulary deflected when a load is applied at the end-effector. It is found in this study that the additional link deflection resulting from the angular deflection of a robotic revolute joint substantially contributes to the end-effector's total deflection. This article presents an improved method via a combination of classical beam theory, energy methods and the concepts of differential …
Intelligent Control Of A Robotic Arm Using Hierarchical Neural Network Systems, Xavier J. R. Avula, Luis C. Rabelo
Intelligent Control Of A Robotic Arm Using Hierarchical Neural Network Systems, Xavier J. R. Avula, Luis C. Rabelo
Chemical and Biochemical Engineering Faculty Research & Creative Works
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 …
Hierarchical Neurocontroller Architecture For Intelligent Robotic Manipulation, Xavier J. R. Avula, Luis C. Rabelo
Hierarchical Neurocontroller Architecture For Intelligent Robotic Manipulation, Xavier J. R. Avula, Luis C. Rabelo
Mechanical and Aerospace Engineering Faculty Research & Creative Works
A hierarchical neurocontroller architecture consisting of two artificial neural network systems for the manipulation of a robotic arm is presented. The higher-level neural system participates in the delineation of the robot arm workspace and coordinates transformation and the motion decision-making process. The lower one provides the correct sequence of control actions. The capabilities, including speed, adaptability, and computational efficiency, of the developed architecture are illustrated by an example.