Inverse Kinematic Model of Flexure-Based Microsurgical Manipulator

Abstract

Abstract-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.

Experimental characterization of a binary actuated parallel manipulator

“…However, often existing parallel manipulator are complex and expansive. A possible solution for achieving low-cost user-friendly design features for small-size manipulators can be the use of flexural joints, as proposed for example in [3][4][5][6][7][8][9][10][11]. Often this design solution is used in combination with shape memory alloy actuators (SMA).…”

“…Accordingly, flexural joints design must be validated with a very careful FEM analysis for achieving a feasible and durable design solution that can be properly obtained by CNC machining of a single bulk piece of material. Further details and discussion on these aspects can be found for example in [3][4][5][6][7]. Additionally, the use of shape memory alloy actuators (SMA) often limits motion ranges within 4% of the actuator full length while the resulting workspace is composed of a finite number of reachable poses that have to be carefully identified through binary combinations.…”

Design and control of piezoelectric-driven handheld manipulator for tremor compensation in microsurgery

“…This section describes the inverse kinematics that is used to calculate the actuation input. Avedillo et al [70] of the mechanism is shown in Fig. 5.12.…”