Jun Fujioka

Development of Orientation Measuring System of a Robot Using a Gyroscope. 2nd Report. Proposal of Position and Orientation Calibration Method of a Robot Using Both Laser Tracking System and Gyroscope.

For the purpose of realizing not only positioning but also orientating accuracy of a robot, a position and orientation calibration method is newly proposed and applied to a 6-DOF articulated robot (PUMA 560). The existence of gear transmission errors and gear backlashes of the 4-6th axes of this robot is detected by using a high precision gyroscope and it is proved that these non-geometric errors cause the orientation error of robot arms tip. A position and orientation measuring system using both a laser tracking system and this gyroscope is proposed. A calibration method, which uses both position and orientation data and considers non-geometric errors of 4-6th axes as parameters to be calibrated in the forward kinematics model, is proposed. The positioning accuracy of 0.2mm and roll, pitch, yaw orientating accuracy of (0.17°, 0.10°, 0.10°), which is near the limit value of calibration predicted by the orientating repeatability, are successfully achieved and it is experimentally proved that the proposed measuring system and the calibration method are very effective for improving the orientating accuracy while keeping the high positioning accuracy. The optimal weights of position and orientation data in the least squares estimation process of parameters are also discussed.

Calibration of Kinematic Parameters of a Robot Using Neural Networks by a Laser Tracking System

Almost the industrial robot tasks are performed by the teaching playback method. This method has problem that the laborious and time-consuming online manual teaching is inevitable. Thus, the offline teaching based on the high positioning accuracy is desired. However, a nominal kinematic model does not consider the geometric errors and non-geometric errors. Therefore, some method of calibrating precisely the parameters is required. The kinematic parameters are calibrated by minimizing errors between the measured positions and the predicted ones by nonlinear least square method. After that, the residual errors caused by non-geometric parameters are further reduced by using neural networks.

Study of Wearable Device Using Pressure-Sensitive Electro-Conductive Knitting

This study describes a device using pressure-sensitive electro-conductive knitting called PCK. PCK is electric conductive material made from fiber including PE 70% and SUS 30%. And its resistance changes in the fibers according to the modification. So it can be used as a sensor of bending, stretching and pressure. This knitting when used as sensor, is inexpensive, highly flexible and air permeable. More importantly, a wearable device made of this material have the function as a sensor only by itself. So it is considered that this material can be applied as a sensor in various applications. In this paper, a Data-glove made of PCK and measurements of the device are presented. And a recognizing system using neural network that can recognize the form of human hand from the output of the Data-glove is also presented. This system attained the high rate of recognition of 5 patterns of hand operation.