Inverse kinematic model of flexure-based microsurgical manipulator.
This paper presents the development of an inverse kinematic model for a flexure-based micromanipulator using a feedforward neural network. The manipulator is part of an active handheld instrument designed to cancel hand tremor during microsurgery. The manipulator is actuated using three piezoelectric stacks. Preliminary results yield a mean squared error of 5% of the total stack range of motion, and a maximum error of 14% of the total range of motion.
Avedillo
Juan Garcia
JG
School of Engineering, University of Valladolid, Valladolid, Spain.
Choi
David Y
DY
Riviere
Cameron N
CN