In this paper, we propose a new methodology for autonomous obstacle avoidance control using a bio-inspired optical flow estimation. The main difference with other methods is that we use an image model inspired by the human vision system to define the constraints in the optical flow formulation which includes a Hermite transform (HT) and a perceptive mask. We use a physical robot platform to test the control algorithm, where due to the structure of the chassis a forward, reverse and turn movements were defined. The robot has a RBG camera to capture images of the path and then calculate optical flow estimation. To define velocity and direction robot response we propose a fuzzy controller. Finally, we made some experiments to demonstrate the performance of control navigation, and how responds algorithm using HT and a perceptive mask.


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    Title :

    Bio-inspired Optical Flow-based Autonomous Obstacle Avoidance Control




    Publication date :

    2019-11-01


    Size :

    314319 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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