A vision-based self-calibration method for industrial robots using variable pose constraints

Abstract

Among various geometrical constraints employed for robot self-calibration, the pose constraint by simultaneously restricting the position and orientation of the robot end-effector is the most comprehensive and effective constraint. However, as it is difficult to control the robot to precisely satisfy the pose constraints, a vision-based robot pose measurement system is designed, which mainly consists of two monochrome cameras fixed onto an adjustment stage and a pose target module mounted on the robot end-effector. Variable pose constraints are established when two or more robot poses are measured with two monochrome cameras at a fixed location. Based on the product-of-exponential (POE) formula, a new self-calibration model is formulated for industrial robots using variable pose constraint in which the robot pose errors are expressed in its tool frame and the position errors are decoupled from the orientation measurement errors. Therefore, the proposed self-calibration model is more accurate and robust than the conventional calibration model, in which the robot pose errors are expressed in its base frame and the position errors are coupled with the orientation measurement errors. Both simulations and experiments are conducted to validate the effectiveness of the proposed self-calibration method. Experimental results on the Aubo i5 robot demonstrate that after calibration, the average position error is reduced from 2.47 mm to 0.77 mm, and the average orientation error is reduced from 0.016 rad to 0.0039 rad.