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Computational neural learning formalisms for manipulator inverse kinematics
| Content Provider | NASA Technical Reports Server (NTRS) |
|---|---|
| Author | Barhen, Jacob Iyengar, S. Sitharama Gulati, Sandeep |
| Copyright Year | 1989 |
| Description | An efficient, adaptive neural learning paradigm for addressing the inverse kinematics of redundant manipulators is presented. The proposed methodology exploits the infinite local stability of terminal attractors - a new class of mathematical constructs which provide unique information processing capabilities to artificial neural systems. For robotic applications, synaptic elements of such networks can rapidly acquire the kinematic invariances embedded within the presented samples. Subsequently, joint-space configurations, required to follow arbitrary end-effector trajectories, can readily be computed. In a significant departure from prior neuromorphic learning algorithms, this methodology provides mechanisms for incorporating an in-training skew to handle kinematics and environmental constraints. |
| File Size | 677610 |
| Page Count | 10 |
| File Format | |
| Alternate Webpage(s) | http://archive.org/details/NASA_NTRS_Archive_19900019714 |
| Archival Resource Key | ark:/13960/t55f3qj7k |
| Language | English |
| Publisher Date | 1989-01-31 |
| Access Restriction | Open |
| Subject Keyword | Cybernetics Teleoperators Stability Neural Nets Control Systems Design Inverse Kinematics Data Processing Embedding Manipulators Trajectories Robotics Real Time Operation Algorithms Strategy End Effectors Formalism Ntrs Nasa Technical Reports Server (ntrs) Nasa Technical Reports Server Aerodynamics Aircraft Aerospace Engineering Aerospace Aeronautic Space Science |
| Content Type | Text |
| Resource Type | Article |
