Abstract—In this paper, design of an intelligent autonomous vehicle is presented that can navigate in noisy and unknown environments without hitting the obstacles in its way. The vehicle is made intelligent with the help of two multilayer feed forward neural network controllers namely ‘Hurdle Avoidance Controller’ and ‘Goal Reaching Controller’ with back error propagation as training algorithm. Hurdle avoidance controller ensures collision free motion of mobile robot while goal reaching controller helps the mobile robot in reaching the destination. Both these controllers are trained offline with the data obtained during experimental run of the robot and implemented with low cost AT89C52 microcontrollers. The computational burden on microcontrollers is reduced by using piecewise linearly approximated version of tangent-sigmoid activation function of neurons. The vehicle with the proposed controllers is tested in outdoor complex environments and is found to reach the set targets successfully.

Index Terms—Navigation in complex environments, neural network, hurdle avoidance behavior, goal reaching behavior, real time implementation.

Umar Farooq, Muhammad Amar, and Syed Omar Saleh are with Department of Electrical Engineering, University of The Punjab Lahore (e-mail: engr.umarfarooq@yahoo.com; amar.ete611@yahoo.com; omar_saleh81@yahoo.com).
Muhammad Usman Asad and Athar Hanif are with Department of Electrical Engineering, The University of Lahore (e-mail: usmansad01@hotmail.com; athar.hanif@ee.uol.edu.pk).

 

Cite:Umar Farooq, Muhammad Amar, Muhammad Usman Asad, Athar Hanif, and Syed Omar Saleh, "Design and Implementation of Neural Network Based Controller for Mobile Robot Navigation in Unknown Environments," International Journal of Computer and Electrical Engineering vol. 6, no.2, pp. 83-89, 2014.