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

A. Al-Araji, Attarid K. Ahmed, Mohammed K. Hamzah
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引用次数: 9

Abstract

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.
移动机器人路径规划算法及控制器设计研究
本文介绍了两种不同类型的无碰撞路径规划算法,并针对移动机器人设计了一种非线性多输入多输出(MIMO)比例积分导数(PID)改进的Elman神经网络(MENN)控制器。提出的两种算法分别是圆形路线图算法(CRM)和粒子群算法(PSO),前者是一种经典的避障和确定目标点的智能方法。设计了非线性MIMO-PID-MENN控制器,用于引导移动机器人在连续路径跟踪过程中通过静态障碍物导航,并利用智能在线算法(PSO)对控制器的变量控制增益进行查找和调整,以获得移动机器人平台的近最优力矩动作。MATLAB数值仿真结果表明,本文提出的算法在平滑、短距离避开静态障碍物、在线性能指标值最小、迭代次数最少的情况下,对于规划理想路径方程和产生理想转矩作用具有较高的精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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