According to D-H method , we built the coordinate systems for each joint of the AACMM, as shown in Figures 1 and 2.
With this ability, BPNN can predict the errors of AACMMs caused by various factors, which makes it possible to compensate for the comprehensive errors and improve the movement uncertainty of AACMM.
Theoretically, it is shown that the model of neural network (2) can be used to simulate the AACMM model (1) with arbitrarily accuracy and hence can compensate for the modeling errors.
According to (2), it is known that the input of the model is the six joint angles of the AACMM and the output is the coordinates of the probe.
In this paper, we propose an improved modeling and parameter identification method for AACMM robotic system.
The advantages and the distinctive features of this proposed identification method in comparison to some other identification methods for AACMM (e.g., [12,15,16]) are as follows:
As shown in Figure 1, the structure of the AACMM is similar to an articulated robot.
According to the DH method, there are four groups of structural parameters in the AACMM, for example, linkage length [d.sub.i], joint length [a.sub.i], torsion angle [[alpha].sub.i], and joint angle [[theta].sub.i].
The determination of the number of required specific positions is not generalizable from one AACMM to another, since the errors committed by each arm will depend on their configuration and assembly defects.
presented a kinematic parameter estimation technique, which allows us to improve the repeatability of the AACMM by more than 50%.
developed a virtual sphere plate, having a standard deviation of around 0.02 mm and a measurement uncertainty from 0.02 to 1.64 [micro]m in point measurements, to evaluate the measurement performance of AACMM's.
The aim of this work is to improve the accuracy of an AACMM by means of a new calibration procedure based on laser tracker multilateration.