Path Planning and Control Strategy Design for Mobile Robot Based on Hybrid Swarm Optimization Algorithm

Abstract

This paper presents an improvement of the actual output trajectory tracking performance of a mobile robot based on convolutional neural network controller with off-line and on-line tuning Back-Propagation algorithms. The goals of this strategy are to find the optimal path to direct its movement and to design Convolutional Neural Network Trajectory Tracking (CNNTT) controller in order to control the nonlinear kinematics mobile robot system. Therefore, a hybrid swarm optimization algorithm uses for solving the two most important problems of path planning; the first is that the path must avoid collision with obstacles, and the second it must reduce the length of the path to a minimum. This paper will discuss the finding of the shortest path with the optimum cost function by using three optimizations’ algorithms; Chaotic Particle Swarm Optimization (CPSO) algorithm, A-star algorithm, and a hybrid swarm optimization algorithm (ACPSO). The task of the proposed feedback (CNNTT) controller is to obtain precisely and quickly the robust left and right wheels velocity which are used to control the position and orientation of the mobile robot system. These algorithms are simulated by MATLAB in a fixed obstacles environment to show the effectiveness of the hybrid swarm optimization algorithm in terms of the minimum number of an evaluation function and the shortest path length as well as the results of the proposed method showing that the (CNNTT) controller is accurate in terms of the mobile robot follows the desired paths quickly through fast obtaining the (CNNTT) controller’s parameters and a smooth linear wheels velocity actions are generated for mobile robot system with minimum number of cost-function evolutions that minimized the tracking error x-position around 4 cm and y-position around 2.5 cm and zero approximately orientation error as well as no oscillation in the responses. Finally, we confirm the effectiveness of the numerical simulation results of the proposed control strategy through comparison other types of controller simulation results.