Inaccurate positioning of the robot end effector causes joint deformation as well as geometric errors when an industrial robot has a payload at its end effector. We propose a new approach of calibration which deals with joint angle dependent errors to compensate for these phenomena. To implement this method, we divided the robot workspace into several local regions, and built a calibration equation by generating the constraint conditions of the end effector’s motion in each local region using a three dimensional position measurement system.
The parameter errors obtained this way were interpolated using the Radial Basis Function Network (RBFN) so as to estimate calibration errors in the regions that we did not measure.
Identified parameter errors were used to correct joint angles in order to improve positioning accuracy of robot end-effector, since it is impossible to get inverse kinematics with these identified parameters. The optimally compensated joint angles obtained were also generalized using the RBFN. In this case, we optimized RBFN structure such that it had small output error with small number of neurons. This optimization were accomplished by genetic algorithm.
We used this technique to improve the performance of a six DOF industrial robot used for arc welding, and used test standard from ISO.
