An adaptive fuzzy system for the control of the vergence angle on a robotic head

An important issue in realizing robots with stereo vision is the efficient control of the vergence angle. In an active robotic vision system the vergence angle along with the pan and tilt ones determines uniquely the fixation point in the 3D space. The vergence control involves the adjustment of the angle between the two cameras’ axes towards the fixation point and, therefore, it enables the robot to perceive depth and to compute obstacle maps. Vergence movement is directly related to the binocular fusion. Additionally, the decision for convergence or divergence is extracted either by motion affine models or by mathematical ones. In this paper, a new method for extracting the cameras’ movement direction is presented. The movement decision is performed by an adaptive fuzzy control system, the inputs of which are the zero-mean normalized cross correlation (ZNCC) and the depth estimations at each time step. The proposed system is assessed on a 4 d.o.f. robotic head, yet it can be utilized in any active binocular system, since it is computationally inexpensive and it is independent to a priori camera calibration.

[1]  Hanspeter A. Mallot,et al.  Vergence Control and Disparity Estimation with Energy Neurons: Theory and Implementation , 2002, ICANN.

[2]  Antonios Gasteratos,et al.  COMPARISON AND EVALUATION OF SIMILARITY MEASURES FOR VERGENCE ANGLE ESTIMATION , 2003 .

[3]  Alexandre Bernardino,et al.  Vergence control for robotic heads using log-polar images , 1996, Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. IROS '96.

[4]  Xuejie Zhang,et al.  Vergence Control of 2 DOF Pan-Tilt Binocular Cameras using a Log-Polar Representation of the Visual Cortex , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[5]  Tara L. Alvarez,et al.  Dynamic assessment of disparity vergence ramps , 2007, Comput. Biol. Medicine.

[6]  Georgios Ch. Sirakoulis,et al.  Review of stereo matching algorithms for 3D vision , 2007 .

[7]  Darius Burschka,et al.  Advances in Computational Stereo , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Antonios Gasteratos,et al.  Development of a stereo vision system for remotely operated robots: A control and video streaming architecture , 2008, 2008 IEEE Conference on Virtual Environments, Human-Computer Interfaces and Measurement Systems.

[9]  Silvio P. Sabatini,et al.  Version and vergence control of a stereo camera head by fitting the movement into the Hering's law , 2007, Fourth Canadian Conference on Computer and Robot Vision (CRV '07).

[10]  Minho Lee,et al.  Biologically motivated vergence control system using human-like selective attention model , 2006, Neurocomputing.

[11]  Y. J. Tejwani,et al.  Robot vision , 1989, IEEE International Symposium on Circuits and Systems,.

[12]  B.E. Shi,et al.  Robotic gaze and vergence control via disparity energy neurons , 2008, 2008 11th International Workshop on Cellular Neural Networks and Their Applications.

[13]  Antonios Gasteratos.  Tele-Autonomous Active Stereo-Vision Head , 2008 .

[14]  Jarno Vanne,et al.  Block-level parallel processing for scaling evenly divisible images , 2005, IEEE Transactions on Circuits and Systems I: Regular Papers.

[15]  Giulio Sandini,et al.  Dynamic Vergence Using Log-Polar Images , 1997, International Journal of Computer Vision.

[16]  Giulio Sandini,et al.  Disparity Estimation on Log-Polar Images and Vergence Control , 2001, Comput. Vis. Image Underst..

[17]  A. Bernardino.  Correlation Based Vergence Control Using Log-polar Images ? , 1996 .

[18]  A. Gasteratos,et al.  Fuzzy vergence control for an active binocular vision system , 2008, 2008 7th IEEE International Conference on Cybernetic Intelligent Systems.