An Intelligent Cognitive System Design for Mobile Robot based on Optimization Algorithm

Abstract

in this paper, a cognitive system based on a nonlinear neural controller and an intelligent algorithm is used to guide an autonomous mobile robot during continuous path-tracking. The controller will navigate over solid obstacles with avoidance. The goal of the proposed structure is to plan and track the reference path equation for the autonomous mobile robot in the mining environment in order to avoid the obstacles and reach to the target position by using intelligent optimization algorithms. Particle Swarm Optimization and Artificial Bee Colony algorithms are used for finding the solutions of the mobile robot navigation problems in the mine by searching the optimal paths. The algorithms are used for finding the reference path equation of the optimal path, as well as finding and tuning the neural control gains values online for the nonlinear neural controller. The goal is to obtain the best torques actions of the wheels for the mining autonomous mobile robot. Simulation results are done by using MATLAB which showed that the proposed cognitive system is more accurate in terms of planning a reference path to avoid obstacles, online finding, and tuning parameters of the controller which generates smooth control actions without saturation for tracking the reference path equation, as well as minimizing the mobile robot tracking pose error.