Automated Tool Coordinate Calibration System of an Industrial Robot

Due to the widespread use of industrial robots in market, its application has extended to welding, painting, and freight handling. And tool coordinate calibration is regularly modified after tool replacement due to collision accident or routine maintenance. After tool replacement, operators often rebuild tool coordinates. This is the traditional mode of operation in the current industrial practices. However, smart factory will make artificial intelligence method replace manual method. This paper presents a system independent method for automatic calibration of the tool coordinate system which is faster, simpler, cheaper and more effective than the manual method. The proposed method required images to be captured using two “eye to hand” cameras and one “eye in hand” camera. Tool position data is then acquired through CamShift and MeanShift algorithm for image trajectory tracking along with coordinate system conversion, several methods like PCA, LDA can deal with the vision data. Optimal Deep Neural Network (DNN) method error compensation of a robot allows the tool to automatically run with the calibration system functions. We have developed a 6 degrees of freedom(DoF) industrial robot for this experiment. Nine different kinds of DNN models are built and finally with suitable tool coordinate error compensation for the current robot, tool calibration can be achieved adaptively and efficiently.