Volume 6, Issue 1, March 2018, Page: 20-30
ANFIS-Based Visual Pose Estimation of Uncertain Robotic Arm Using Two Uncalibrated Cameras
Aung Myat San, Department of Mechatronic Engineering, Mandalay Technological University, Mandalay, Myanmar
Wut Yi Win, Department of Mechatronic Engineering, Mandalay Technological University, Mandalay, Myanmar
Saint Saint Pyone, Department of Mechatronic Engineering, Mandalay Technological University, Mandalay, Myanmar
Received: Jan. 2, 2018;       Accepted: Jan. 17, 2018;       Published: Feb. 6, 2018
DOI: 10.11648/j.wcmc.20180601.13      View  1417      Downloads  49
Abstract
This paper describes a new approach for the visual pose estimation of an uncertain robotic manipulator using ANFIS (Artificial Neuro-Fuzzy Inference System) and two uncalibrated cameras. 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. The vision system is composed of two cameras; installed on the top and on the lateral side of the robot, respectively. These two cameras need no calibration; thus, they can be installed in any position and orientation with just the condition that the end-effector of the robot must remain always visible. A red-colored feature point is fixed on the end of the third robotic arm link. In this study, captured image data via two fixed-cameras vision system are used as the sensor feedback for the position tracking of an uncertain robotic arm. LabVolt R5150 manipulator in our laboratory is used as case study. The visual estimation system is trained using ANFIS with subtractive clustering method in MATLAB. In MATLAB, the robot, feature point and cameras are simulated as physical behaviors. To get the required data for ANFIS, the manipulator was maneuvered within its workspace using forward kinematics and the feature point image coordinates were acquired with the two cameras. Simulation experiments show that the location of the robotic arm can be trained in ANFIS using two uncalibrated cameras; and problems for computational complexity and calibration requirement of multi-view geometry can be eliminated. Observing Mean Square Error (MSE), Root Mean Square Error (RMSE), Error Mean and Standard Deviation Errors, the performance of the proposed approach is efficient for using as visual feedback in uncertain robotic manipulator. Further, the proposed approach using ANFIS and uncalibrated vision system has better in flexibility, user-friendly manner and computational concepts over conventional techniques.
Keywords
ANFIS, Forward Kinematics, Two Uncalibrated Cameras, LabVolt R5150 Robot
To cite this article
Aung Myat San, Wut Yi Win, Saint Saint Pyone, ANFIS-Based Visual Pose Estimation of Uncertain Robotic Arm Using Two Uncalibrated Cameras, International Journal of Wireless Communications and Mobile Computing. Vol. 6, No. 1, 2018, pp. 20-30. doi: 10.11648/j.wcmc.20180601.13
Copyright
Copyright © 2018 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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