Redundancy resolution of robotic manipulators with neural computation

Abstract

This letter presents a neural-network-based computational scheme for redundancy resolution of manipulators. The Tank-Hopfield (TH) network is adopted for pseudoinverse and inverse kinematics calculations and it can provide joint velocity and joint acceleration solutions within a time frame of the order of hundreds of nanoseconds. Incorporating the TH network into the redundancy resolution scheme allows planning algorithms to be implemented in real time. Simulation results for a three-link planar manipulator are presented to demonstrate that the proposed approach is efficient and practical.