A vision based feedback mechanism for 6-DOF Stewart Platform

Eyes are the most ubiquitous sensors of humankind. However, ``Vision Based Sensors" are not as common as what this fact would suggest. A survey of the previous work on image processing would reveal that the bulk of the research in this area have been conducted since the year 2000. These facts clearly show that image processing is the technology of our century, and is very open to further development. In this work, a system that combines a classical mechanism of the 20th century with the recent technology of the 21st century is presented and the design, theoretical background, the software algorithm for a vision based, low cost feedback mechanism is given. Simulation results based on the image processing of a 3-D animated model of a Stewart Platform through the aforementioned feedback system are also included. These results demonstrate that the software performed all necessary tasks to compute the leg lengths successfully.

Anahtar Kelimeler:

Stewart platform, image recognition, feedback mechanism

A vision based feedback mechanism for 6-DOF Stewart Platform

Keywords:

Stewart platform, image recognition, feedback mechanism,

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Results
This work shows that it would be possible to get very satisfactory and low cost results from a feedback system built with components easily obtained from a computer hardware store, printouts of sign squares and special software designed for this purpose. This eliminates the need for the very high cost hardware feedback systems. The cost advantage and relative simplicity of the system suggest that we may see these systems used frequently in the future.
The performance of the system is open to improvements with the increases in camera resolutions and PC computing power. The plans to implement this system on a real Stewart Platform as the next step are in place and the work to achieve this is already under way.
D. Stewart, “A Platform with Six Degrees of Freedom”, Proc. Instn. Mech. Engrs., vol.80, pp.371-386, 1965.
E. Anli, H. Alp, S.N. Yurt, I. Ozkol. “Paralel mekanizmalarin kinematigi, dinamigi ve calisma uzayi.” Havacilik ve uzay teknolojileri dergisi. Volume 2 Issue 1 (19-36), January 2005.
C.C. Nguyen, Z.L. Zhou, S.S. Antrazi, C.E. Campbell Jr. “Eﬃcient Computation of Forward Kinematics and Jacobian Matrix of a Stewart Platform-based Manipulators.” Southeastcon, IEEE Proceedings of, 7-10 Apr 1991. [4] P. Nanua, K.J. Waldron, V. Murthy. “Direct Kinematic Solution of a Stewart Platform.” Robotics and Automation, IEEE Transactions on,1990.
B. Dasgupta, T. S. Mruthyunjaya. “A Newton-Euler Formulation for the Inverse Dynamics of the Stewart Platform Manipulator.” Mech. Mach. Theory Vol. 33, No. 8, pp. 11351152, 1998.
S.H. Lee, J.B. Song, W.C. Choi, D. Hong. “Position Control of a Stewart Platform Using Inverse Dynamics Control with Approximate Dynamics.” Mechatronics 13 605-619, 2003.
I. Davliakos, E. Papadopoulos. “A Model-Based Impedance Control of a 6-Dof Electrohydraulic Stewart Platform.” European Control Conference 2007, Kos, Greece, July 2-5, 2007.
Y. Ting, Y.S. Chen, H.C. Jar. “Modeling and Control for a Gough-Stewart Platform CNC Machine.” Journal of Robotic Systems 21(11), 609-623, 2004.
V.E. Omurlu, U. Buyuksahin, I. Yildiz, A. Unsal, A. Sagirli, S.N. Engin, I.B. Kuukdemiral , “A Stewart Platform as a FBW Flight Control Unit for Space Vehicles.” Recent Advances in Space Technologies - RAST 2009, Istanbul, Turkiye, 11-13 June, 2009.
Y.P. Huang. “A Back Propagation Based Real-Time License Plate Recognition System.” International Journal of Pattern Recognition and ArtiŞcial Intelligence Vol. 22, No. 2 233-251, 2008.
O. Mendoza, P. Melin, G. Licea. “A Hybrid Approach for Image Recognition Combining Type-2 Fuzzy Logic, Modular Neural Networks and the Sugeno Integral.” Information Sciences 179 2078-2101, 2009.
G. Li, L. Min, H. Zang. “Color Edge Detections Based on Cellular Neural Network. International Journal of Bifurcation and Chaos”, Vol. 18, No. 4 1231-1242, 2008.
J. Wu, Y. (J.) Tsai. “Enhanced Roadway Inventory Using a 2-D Sign Video Image Recognition Algorithm.” Computer-Aided Civil and Infrastructure Engineering 21 369-382, 2006.
K.A. Hwang, C.H. Yang. “Learner Attending Auto-Monitor in Distance Learning Using Image Recognition and Bayesian Networks.” Expert Systems with Applications 36, 11461-11469, 2009.
Y. Tao, Z. Chen, C.L. Griﬃs. “Chick Feather Pattern Recognition.” IEE Proc.-Vis. Image Signal Process., Vol. 151, No. 5, October 2004.