Development of a Path Planning Algorithms and Controller Design for Mobile Robot

This paper presents the two different types of the collision-free path planning algorithms, and a nonlinear Multi-Input Multi-Output (MIMO) Proportion Integral Derivative (PID) Modified Elman Neural Network (MENN) controller design for the mobile robot. The two proposed algorithms are Circular Road Map (CRM) algorithm as a classical method and Particle Swarm Optimization (PSO) algorithm an intelligent method to avoid the obstacles and determine the target point. The proposed nonlinear MIMO-PID-MENN controller is designed to guide the mobile robot during the continuous path-tracking through static obstacles navigation with the intelligent on-line algorithm (PSO) is used to find and tune the variable control gains of the proposed controller to obtain the near optimal torques actions for the mobile robot platform. The numerical MATLAB simulation results show that the proposed algorithms have high accuracy for planning the desired path equation and generating a perfect torque action in terms of avoiding the static obstacles with a smooth and short distance and minimizing the on-line performance index value as well as a minimum number of iterations.