On The Comparison of Fuzzy Interpolations and Neural Network Fitting Functions in Modeless Robot Calibrations

Abstract

A comparison study among type 1 fuzzy interpolations (T1FI), interval type 2 fuzzy interpolations (IT2FI) and neural network fitting functions (NNFF) applied on modeless robots calibrations is proposed. Traditional robots calibration implements either model or modeless method. The compensation of position error in modeless method is to move the robot's end-effector to a target position in the robot workspace, and to find the target position error based on the measured neighboring 4-grid-point errors around the target position. A camera or other measurement device is attached on the robot's end-effector to find and measure the neighboring position errors, and compensate the target position with various error interpolation methods. By using the NNFF technique provided in this paper, the accuracy of the position error compensation can be greatly improved, which has been confirmed by the simulation results given in this paper. Compared with some other popular interpolation methods, this NNFF technique is a better choice. The simulation results show that more accurate compensation result can be achieved using this technique compared with the type-1 fuzzy and interval type 2 fuzzy interpolation methods.