基于Raspberry和Arduino串行通信的HSV滤波方法实现自动驾驶汽车系统

Kelvin Kristian Roestamadji, F. B. Setiawan, L. Pratomo, Slamet Riyadi
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引用次数: 0

摘要

在这个时候,交通运输领域的技术发展变得越来越重要。因此,该公司进行了创新,创造了一款可以在高度安全的情况下自动驾驶的汽车。在本研究中,我们设计了一个1:10比例RC汽车的自动驾驶系统,使用树莓派4和树莓派相机形式的主要组件作为自动驾驶汽车自动控制的图像处理。然后利用Arduino Nano、BTS7960、Driver L298N等器件来调节直流电机的运动。在这篇文章中,将展示这种自动驾驶汽车的控制策略,该策略将用于检测车道,作为自主行走的指南。本研究采用HSV颜色滤波方法结合形态学技术检测待通过路径。与之前研究的使用待通过采样路径的CNN方法相比,本研究的路径检测非常准确,并且可以实时操作。检测到路径后,微型计算机与单片机之间的互连将工作,同步路径检测和电机运动。在人工智能自动驾驶汽车实验室进行的试验和硬件实现中,它可以根据创建的算法工作,成功率为90%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Implementation of Self Driving Car System with HSV Filter Method Based on Raspberry & Arduino Serial Communication
The development of technology in the transportation sector at this time is increasingly crucial. So the company innovates to create a car that can run itself with a high level of security. In this study, we designed an autonomous drive system for a 1:10 scale RC car using the main components in the form of a Raspberry Pi 4 and a Raspberry Pi camera as image processing for automatic control of an self driving car. Then the Arduino Nano, BTS7960, and Driver L298N components are used to regulate the movement of the DC motor. In this article, the control strategy of this self-driving car will be shown which will be implemented to detect lanes as a guide to walk autonomously. This study uses the HSV color filer method with morphology techniques to detect the path to be passed. This study resulted in a path detection that was very accurate and operated in real-time when compared to the CNN method using sampling paths to be passed that had previously been researched. After the path is detected, the interconnection between the mini computer and the microcontroller will work to synchronize the path detection and motor movement. In trials and hardware implementations carried out in the self-driving car laboratory with artificial intelligence, it can work according to the algorithm created with a success rate of 90%.  
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