Membership function modification of fuzzy logic controllers with histogram equalization

Improve the robot calibration accuracy using a dynamic online fuzzy error mapping system

Traditional robot calibration implements model and modeless methods. The compensation of position error in modeless method is to move the end-effector of robot to the target position in the workspace, and to find the position error of that target position by using a bilinear interpolation method based on the neighboring 4-point's errors around the target position. A camera or other measurement devices can be utilized to find or measure this position error, and compensate this error with the interpolation result. This paper provides a novel fuzzy interpolation method to improve the compensation accuracy obtained by using a bilinear interpolation method. A dynamic online fuzzy inference system is implemented to meet the needs of fast real-time control system and calibration environment. The simulated results show that the compensation accuracy can be greatly improved by using this fuzzy interpolation method compared with the bilinear interpolation method.