Geometric Error Identification for 6DoF Robotic Manipulator Calibration to Improve Absolute Positioning Accuracy

As robotic manipulators become extensively incorporated in various modern industries, there is a growing list of applications for human-to-robot interaction and robot-to-robot collaboration, which requires strong performance on the absolute positioning accuracy of the robot. The lack of accuracy could come from extreme operating environments, manufacturing and assembly errors, dynamic influence from gear compliance and backlash, etc. This thesis tackles the accuracy issue from two aspects: tighter mechanical tolerances and a closer matching kinematics model with the actual robot. For these purposes, according to the geometry of a pneumatically driven six DoF manipulator, a 6D parametric kinematics model is firstly derived. The proposed model is highly flexible in terms of introducing, anywhere in each linkage of the manipulator, any number of virtual mechanical tolerance points that lump effects of dimension and orientation deviations caused by mechanical tolerances. Therefore, concerned mechanical tolerances can be added to the model and studied through Fuzzy arithmetic to analyze their influence on the TCP position. Meanwhile, geometric errors are also the primary source of discrepancies between the nominal model and real hardware. The model can include translational and rotational error parameters that need identification to quantify the effects from the geometric errors at the locations of the virtual mechanical tolerance points. For an effective identification, dependent error parameters are systematically eliminated using QR decomposition. Once the model reduction is completed, the nonlinear least-squares optimization problem using the Gauss-Newton line search method is formulated to identify the remaining independent error parameters. The identification process is eventually verified on the experimental manipulator. In a nutshell, the thesis presents 1) a tolerance analysis tool that offers insights for potential targeted manufacturing improvements to decrease the dominant tolerances, and 2) a capable parameter identification process that rectifies the nominal kinematics model to agree with the hardware.M.S