Robotics Research Journal (RRJ)

Research Article

Improve Calibration Accuracy of Modeless Robots by Using a Shallow Neural Network

Ying Bai¹ and Dali Wang ²

¹Dept of Computer Science & Engineering, Johnson C. Smith University, Charlotte, NC 28216

²Dept of Physics, Comp Sci and Engineering, Christopher Newport University, Newport News, VA 23606

Submitted: August 7, 2018; Revised: December 10, 2018; Accepted: December 12, 2018

Abstract

This paper describes a technique for the position error estimations and compensations of the modeless robots and manipulators calibration process based on a shallow neural network fitting function method. Unlike traditional model-based robots calibrations, the modeless robots calibrations do not need to perform any modeling and identification processes. Only two processes, measurements and compensations, are necessary for this kind of robots calibrations. 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-point errors around the target position. A camera or other measurement device may be attached on the robot’s end-effector to find or measure the neighboring position errors, and compensate the target positions with the interpolation results. By using the shallow neural network fitting technique, the accuracy of the position error compensation can be greatly improved, which is confirmed by the simulation results given in this paper. Also the comparisons among the popular traditional interpolation methods, such as bilinear and fuzzy interpolations, and this shallow neural network technique, are made via simulation studies. The simulation results show that more accurate compensation result can be achieved using the shallow neural network fitting technique compared with the bilinear and fuzzy interpolation methods.

Keywords

robotics; modeless robots calibrations; fuzzy interpolations; artificial neural networks

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