Bio-inspired Optical Flow Interpretation with Fuzzy Logic for Behavior-Based Robot Control

This paper presents a bio-inspired approach for optical flow data interpretation based on fuzzy inference decision making for visual mobile robot navigation. The interpretation results of regionally averaged optical flow patterns with pyramid segmentation of the optical flow field deliver fuzzy topological and topographic information of the surrounding environment (topological structure from motion). It allows a topological localization in a global map as well as controlled locomotion (obstacle avoidance, goal seeking) in a changing and dynamic environment. The topological optical flow processing is embedded in a behavior based mobile robot navigation system which uses only a mono- camera as primary navigation sensor. The paper discusses the optical flow processing approach as well as the rule based fuzzy inference algorithms used. The implemented algorithms have been tested successfully with synthetic image data for a first verification and parameter tuning as well as in a real office environment with real image data.

[1]  Klaus Janschek,et al.  Performance Analysis for Visual Planetary Landing Navigation Using Optical Flow and DEM Matching , 2006 .

[2]  Ronald C. Arkin,et al.  An Behavior-based Robotics , 1998 .

[3]  Paolo Pirjanian,et al.  Behavior Coordination Mechanisms - State-of-the-art , 1999 .

[4]  Berthold K. P. Horn,et al.  Determining Optical Flow , 1981, Other Conferences.

[5]  Edward Tunstel,et al.  Mobile Robot Autonomy Via Hierarchical Fuzzy Behavior Control , 1996 .

[6]  Hanspeter A. Mallot,et al.  Biomimetic robot navigation , 2000, Robotics Auton. Syst..

[7]  Dario Floreano,et al.  Fly-inspired visual steering of an ultralight indoor aircraft , 2006, IEEE Transactions on Robotics.

[8]  Klaus Janschek,et al.  An Embedded Optical Flow Processor for Visual Navigation using Optical Correlator Technology , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[9]  Zhang,et al.  Honeybee navigation en route to the goal: visual flight control and odometry , 1996, The Journal of experimental biology.

[10]  Christopher E. Neely,et al.  Mixed-mode VLSI optic flow sensors for in-flight control of a micro air vehicle , 2000, SPIE Optics + Photonics.

[11]  A. Verri,et al.  Green Theorems and Qualitative Properties of the Optical Flow , 1991 .

[12]  N. H. C. Yung,et al.  An intelligent mobile vehicle navigator based on fuzzy logic and reinforcement learning , 1999, IEEE Trans. Syst. Man Cybern. Part B.

[13]  S. Sivanandam,et al.  Introduction to Fuzzy Logic using MATLAB , 2006 .

[14]  Henrik I. Christensen,et al.  Constrained structure and motion estimation from optical flow , 2002, Object recognition supported by user interaction for service robots.

[15]  Carlo Tomasi,et al.  Comparison of approaches to egomotion computation , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[16]  Steven S. Beauchemin,et al.  The computation of optical flow , 1995, CSUR.

[17]  Xiao Peng,et al.  Fuzzy behavior-based control of mobile robots , 2004, IEEE Transactions on Fuzzy Systems.

[18]  K. Prazdny,et al.  Egomotion and relative depth map from optical flow , 2004, Biological Cybernetics.

[19]  G. Nalbach.  The halteres of the blowfly Calliphora , 1993, Journal of Comparative Physiology A.

[20]  Rama Chellappa,et al.  Accuracy vs Efficiency Trade-offs in Optical Flow Algorithms , 1996, Comput. Vis. Image Underst..

[21]  M. Srinivasan,et al.  Range perception through apparent image speed in freely flying honeybees , 1991, Visual Neuroscience.

[22]  Tomas Plachetka,et al.  POV||Ray: PERSISTENCE OF VISION PARALLEL RAYTRACER , 1998 .

[23]  William H. Warren,et al.  Robot navigation from a Gibsonian viewpoint , 1994, Proceedings of IEEE International Conference on Systems, Man and Cybernetics.

[24]  Karen Roberts,et al.  Centering behavior using peripheral vision , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[25]  Javier Díaz,et al.  FPGA-based real-time optical-flow system , 2006, IEEE Transactions on Circuits and Systems for Video Technology.