基于优化算法的移动机器人智能认知系统设计

A. Al-Araji, K. Dagher, Bakir A. Ibraheem
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引用次数: 4

摘要

本文提出了一种基于非线性神经控制器和智能算法的认知系统来引导自主移动机器人进行连续路径跟踪。控制器将导航避开固体障碍物。该结构的目标是利用智能优化算法规划和跟踪自主移动机器人在采矿环境中的参考路径方程,以避开障碍物并到达目标位置。采用粒子群算法和人工蜂群算法,通过搜索最优路径来求解矿山移动机器人导航问题。该算法用于寻找最优路径的参考路径方程,以及在线寻找和调整非线性神经控制器的神经控制增益值。目标是获得采矿自主移动机器人的最佳车轮力矩动作。仿真结果表明,所提出的认知系统在规划避障参考路径、在线寻优、控制器参数整定生成平滑无饱和跟踪参考路径方程的控制动作、最小化移动机器人跟踪位姿误差等方面具有较高的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Intelligent Cognitive System Design for Mobile Robot based on Optimization Algorithm
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.
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