A development of visual-feedback automatic control for robotic manipulator
Article Sidebar
Main Article Content
Abstract
In this work, an automatic robotic manipulator control based on visual information for object tracking is presented. The tracking process employ Recurrent Neural Network and Kalman filter techniques to predict the location of target object which is used to calculate movement of the robot. A manipulator control process was developed to provide consistent motion and reduce vibration. We also propose an object analysis based on image processing technique for object size determination. We implemented the proposed system with an actual robotic manipulator with a gripper for experiments. The experiments shown that object size estimation method results the maximum accuracy at 1.13%. The proposed object tracking and manipulator control process achieved vibration reduction 3.05 times compared to simple control method and reduced the time of end-effector reaching the target object by 1.29sec.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
References
W. Wolfslag, M. Plooij, R. Babu ska and M. Wisse, "Learning robustly stable open-loop motions for robotic manipulation," Robotics and Autonomous Systems, April 2015.
S. Hutchinson, G. Hager and P. Cork, "A Tutorial on Visual Servo Control," IEEE Transactions on Robotics and Automation, November 1996.
P. I. Corke, VISUAL CONTROL: High-Performance Visual Servoing, CSIRO Division of Manufacturing Technology, Australia., 1996.
a. S. H. F. Chaumette, "Visual Servo Control Part I: Basic Approaches," IEEE Robotics and Automation Magazine, vol. 13(4), pp. 82-90, 2006.
J. Wu, Introduction to Convolutional Neural Networks, National Key Lab for Novel Software Technology, May 1,2017.
R. L. Galvez, A. A. Bandala, E. P. Dadios, R. R. P. Vicerra and J. M. Z. Maningo, "Object Detection Using Convolutional Neural," in TENCON 2018 - 2018 IEEE, Jeju, 2018.
B. Qiang, R. Chen, M. Zhou, Y. Pang, Y. Zhai and M. Yang, "Convolutional Neural Networks-Based Object Detection Algorithm by Jointing Semantic Segmentation for Image," MDPI Sensors 2020, 2020.
J. Fan, W. Xu, Y. Wu and Y. Gong, "Human Tracking Using Convolutional Neural Networks," IEEE Transactions on Neural Networks, 2010.
M. Erdmann and M. T. Mason, "An Exploration of Sensorless Manipulation," IEEE Journal on Robotics and Automation, September 1988.
W. Zhang, Y. Luo, Z. Chen, Y. Du, D. Zhu and P. Liu, "A Robust Visual Tracking Algorithm Based on Spatial-Temporal Context Hierarchical Response Fusion," MDPI Article Algorithms, December 2019.
F. Lotf, V. Ajallooeian and H. D. Taghirad, "Robust Object Tracking Based on Recurrent Neural Networks," in International Conference on Robotics and Mechatronics, Tehran, 2018.
D. Gordon, A. Farhadi and D. Fox, "Real-Time Recurrent Regression Networks for Visual Tracking of Generic Objects," in IEEE ROBOTICS AND AUTOMATION LETTERS. PREPRINT VERSION.DECEMBER, 2017.
S. Ren, K. He, R. Girshick and J. Sun, "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks," in IEEE Transactions on Pattern Analysis and Machine Intelligence, Jun 2017.
K. He, G. Gkioxari, P. Dollar and R. Girshick, "Mask R-CNN," in IEEE transactions on pattern analysis and machine intelligence, 2018.
G. Welch and G. Bishop, An Introduction to the Kalman Filter, Chapel Hill, University of North Carolina at Chapel Hill, 2006.
G. Salaganata, Munri and Y. Affryenni, "Moving Object Tracking Using Hybrid Method," in International Conference on Information and Communications Technology (ICOIACT), 2018.
Taylor, Liana; Mirdanies, Midriem; Saputra, Roni Permana, "Optimized object tracking technique using Kalman filter," Mechatronics Electrical Power and Vehicular Technology, July 2016.
H. A. Patel and D. G. Thakore, "Moving Object Tracking Using Kalman," International Journal of Computer Science and Mobile Computing, vol. 2, no. 4, pp. 326-332, 2013.
R. E. Kalman, "A new approach to linear filtering and prediction problems," Journal of Basic Engineering, vol. 82, no. 1, pp. 35-45, 1960.
K. Saho, "Kalman Filter for Moving Object Tracking: Performance Analysis and Filter Design," 20 December 2017.
Y. Kim and H. Bang, "Introduction to Kalman Filter and Its Applications," 5 November 2018.
W. Mokrzycki, "New version of Canny edge detection algorithm," in ICCVG, Olsztyn, January 2012, pp. 533-540.
W. Gongjian and W. Runsheng, "A Survey on Hough Transform, Theory, Techniques and Applications," IJCSI International Journal of Computer Science Issues, vol. 12, no. 1, January 2015.