Calibration of robot kinematics using a double ball-bar with embedded sensing

Today’s industrial robots are highly repeatable, but need to be calibrated to improve their absolute accuracy. This calibration can be done on many of the robot properties such as kinematic parameters, joint friction or bending stiffness. This thesis explores a calibration procedure for the kinematic parameters, that use a specialized piece of hardware - the double ball-bar. The double ball-bar restricts the motion of the robot to a spherical surface. Sensors were added to the ball-bar joints, which made it possible to use the forward kinematic homogeneous transformation matrix from both the robot side and the ball-bar side, the matrices can be compared to each other and the parameters of the robot can be identified using a non-linear least-squares minimization algorithm. The calibration proved promising in simulations and showed an increased robustness to error sources such as white noise and fluctuations of the gear ratio found in cycloid drives. It also provided an improved identification of the robot parameters compared to the calibration done using the sensor-less double ball-bar. In experiments the identification showed some improvement in the identification over using the sensor-less double ball-bar, but also that the method needs to be further improved to be able to produce satisfactory calibration results