A low-cost monocular vision system for robot calibration

The inverse kinematic model uses robot design parameters: ideal link lengths and mounting angles. In practice, these values hardly coincide with the design values due to manufacturing and assembly processes or continuous use of the robot. In order to reduce this geometric error, it is necessary to calibrate the robot to update the geometric model and reduce the resulting error of the robot end-effector. In this work, we propose a methodology based on a vision system to calibrate the robot's geometric parameters and minimize the error between the robot's end-effector theoretical and real trajectory. This way, variations are introduced to the geometric parameters that generate errors between the robot's desired position and the position developed. The results show up a reduction of the average position error of 54.6%.