In this work, compact auto focus actuator moving lens module in the camera phone is developed by applying
piezoelectric single crystal. This actuator is reduced in the size by applying the piezoelectric single crystal. The size of
developed actuator (12*9.2*5.6 mm<sup>3</sup>) is reduced to 60.4% compared with the actuator using general voice coil motor.
From the performance test, the developed actuator has moving stroke of 0.16mm, full stroke hysteresis of 5μm, settling
time of 150msec, maximum overshoot of 7%, and unit step motion of 1μm. The manufacturing method for proposed
actuator can be applicable to the nano-millimeter resolution and sub-millimeter stroke positioning system using for
precision measuring microscope.
Auto focus actuator, which is used to move a lens module in the mobile phone having a camera module, is developed. Camera module containing auto-focus actuator requires to minimize total size because of application area such as mobile phone, digital camera, and personal digital assistant. There are stepping motor, voice coil motor, and piezoelectric motor as auto-focus actuator. In this paper, voice coil motor having new electromagnetic configuration is proposed. And actuator using proposed voice coil motor is developed by magnetic field analysis using finite element method and magnetic circuit analysis. The size of the developed actuator is reduced to 67.3% compared with actuator using previous electromagnetic configuration. From the performance test, the developed actuator has moving stroke of 0.4mm, hysteresis of 20μm, full stroke current of 100mA, and unit step motion of 10μm.
As the environment under which the mobile robot works varies, the characteristic of the sensors for the mobile robot navigation also varies. Thus it is desirable that the sensor is calibrated on-line for more reliable information using the measurements during the mobile robot performs a task. This paper presents an on-line sensor calibration scheme to estimate the unknown sensor bias for mobile robot navigation using the parity vector and recursive minimum variance estimation. The calibration error equation independent of the current position is obtained from the parity vector and then the current position of the mobile robot is estimated from the calibrated sensor data. The validity of the proposed scheme is evaluated through computer simulation.