Trajectory tracking with ANFIS and uncalibrated vision system for uncertain robotic manipulators

Abstract

This paper describes a new approach for tracking the trajectory of uncertain robotic manipulators using ANFIS (Artificial Neuro-Fuzzy Inference System) and uncalibrated vision system. The main emphasis of this work is on the ability to estimate the positioning accuracy and repeatability of a low-cost robotic arm with unknown parameters under uncalibrated vision system. In this study, captured image data are collected from two fixed-cameras vision system; installed on the top and lateral sides of the robot, respectively. For training and validating purposes, the robot is manoeuvring within its workspace using forward kinematics. The tracking system is trained using ANFIS with subtractive clustering method in MATLAB. Extensive simulations were carried out to illustrate the effectiveness of the proposed visual tracking method for LabVolt R5150 manipulator in our laboratory. Observing the simulation results, the performance of the proposed approach is efficient for using the vision-based learning system as visual feedback in uncertain robotic manipulator.