基于Mamdani的轮式移动机器人避障模糊控制器

A. Najmurrokhman, Kusnandar, U. Komarudin, Sunubroto, A. Sadiyoko, Tisna Yanu Iskanto
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引用次数: 3

摘要

移动机器人是一种可以自由移动的机器人,因为它配备了轮子或腿等运动元件。在引导其运动时,移动机器人配备了导航系统,这样它就可以避开障碍物。本文利用模糊逻辑原理,结合Mamdani模糊推理系统,设计并实现了一种轮式移动机器人,使其具有避障能力。移动机器人配备了三对HC-SR04超声波传感器,用于检测机器人与障碍物之间的距离。在Arduino微控制器中安装模糊逻辑控制器,为安装在每个机器人车轮上的直流电机产生驱动信号。整个模糊系统采用超声波传感器获得的三个距离输入,每个变量分别具有三角形和梯形隶属函数的三个模糊集。同时,输出变量为每个车轮的运动速度,每个输出变量有三个模糊集,分别具有三角形和梯形隶属函数。基于适当的规则库,设计了模糊逻辑控制器,使机器人运动时具有避障能力。实验结果表明,轮式移动机器人能够沿轨迹运动而不撞墙,具有一定的避障能力。与Matlab仿真结果相比,安装在Arduino微控制器上的模糊推理系统对部分实验数据绘制的速度值准确率约为96%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mamdani based Fuzzy Logic Controller for A Wheeled Mobile Robot with Obstacle Avoidance Capability
Mobile robot is a type of robot that can move freely because it is equipped with motion elements such as wheels or legs. In guiding its motion, a mobile robot is equipped with a navigation system so that it can avoid obstacles. This paper describes the design and implementation of a wheeled mobile robot using fuzzy logic principles with Mamdani's fuzzy inference system so that the robot has the obstacles avoidance capability. Mobile robot is equipped with three pairs of HC-SR04 ultrasonic sensors to detect the distance between the robot and the obstacles. Fuzzy logic controller is installed in the Arduino microcontroller to generate an actuating signal for the DC motor mounted on each robot wheel. The whole fuzzy system is designed using three distance inputs obtained from ultrasonic sensors with each variable having three fuzzy sets with triangular and trapezoidal membership functions. Meanwhile, the output variable is the speed of movement of each wheels with each output variable has three fuzzy sets with triangular and trapezoidal membership functions. Based on the appropriate rule bases, fuzzy logic controllers are designed to achieve robot motion with the obstacle avoidance capability. The experimental results show the wheeled mobile robot can move along the trajectory without hitting the walls so that it has an obstacle avoidance capability. Compared to simulation results from Matlab, the accuracy of the speed value that rendered by the fuzzy inference system installed in Arduino microcontroller for some experimental data is approximately 96 %.
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