On the identification of robot parameters by the classic calibration algorithms and error absorbing trees

In this paper, we propose a feasible method to construct a virtual manipulator in a 3D graphics environment, which is equivalent to a real manipulator including its dynamics. For this purpose, we first calibrate the parameters of robot dynamics by the two classic algorithms. Unfortunately, both the classic methods are not practically stable because each motion pattern includes noise and error. To overcome this drawback, we indirectly absorb the position differences between experimental and calculated manipulators by three types of learning trees. Moreover, many neighbor dynamic motions are directly memorized by the same learning trees. As a result, when real and virtual robots are independently supervised by the PD control, their angular errors of rotational joints amount to zero. In addition, even though both robots are independently supervised as slave arms from a master arm by the same sequence of forces in a bilateral control based on the PD control, motion sequences of real and virtual slave arms equal to each other. As a result, a virtual manipulator can be used in a 3D graphics animation, which is truly replaced of a real manipulator.