A New Manipulator Calibration Method for the Identification of Kinematic and Compliance Errors Using Optimal Pose Selection

In this study, a manipulator calibration algorithm is suggested to decrease the positional errors of an industrial robotic using genetic select optimal measurement poses. First, based on observability index used for selection By employing selected poses, conventional kinematic identify geometric robot. Finally, further improve accuracy robot, compliance are compensated by radial basis function neural network effective torques. The proposed method provides novel and way poses process enhances robot manipulators constructing relationship between torque function. results experimental validation processes carried out YS100 show effectiveness in comparison with other approaches.