Dynamic learning-based Jacobian estimation for pan-tilt-verge head control

  • Authors:

  • Sila Sornsujitra;Arthit Srikaew

  • Affiliations:

  • Robotics and Automation Research Unit for Real-World Applications, School of Electrical Engineering, Suranaree University of Technology, Nakhon Ratchasima, Thailand;Robotics and Automation Research Unit for Real-World Applications, School of Electrical Engineering, Suranaree University of Technology, Nakhon Ratchasima, Thailand

  • Venue:
  • ACMOS'06 Proceedings of the 8th WSEAS international conference on Automatic control, modeling & simulation
  • Year:
  • 2006

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Abstract

Pan-tilt-verge (PTV) vision system is one of the most widely used in active vision. The main advantage of using such system is its 4 DOF which allows tracking of moving objects efficiently. Besides a physical design of the head, an overall tracking performance of the system depends on its controller. This paper presents a development of controlling PTV head to achieve one of human-like eye movement behaviors, i.e. saccade. The PTV head is driven directly from a controller using visual feedback. The dynamic Jacobian estimation is obtained by using a self-organizing map network with unsupervised learning scheme. The estimated Jacobian is used in PTV head controller and results are desirable for both performance and speed of learning. Moreover, the system can eventually perform tracking without a priori knowledge of the head structure, e.g. mathematical model of the head and hardware calibration. Thus the system can conveniently be implemented.