ADAPTIVE HEADLIGHT CONTROL USING MULTI-SENSORS

This research aims to develop a multi-sensor based adaptive headlight control system for a mobile robot with three different hardware approaches, namely Arduino, ESP32, and Raspberry Pi. Each approach was applied separately to three types of mobile robots to evaluate the performance of each syste...

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Bibliographic Details
Main Author: Sandra
Format: Theses
Language:Indonesia
Online Access:https://digilib.itb.ac.id/gdl/view/87116
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Institution: Institut Teknologi Bandung
Language: Indonesia
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Summary:This research aims to develop a multi-sensor based adaptive headlight control system for a mobile robot with three different hardware approaches, namely Arduino, ESP32, and Raspberry Pi. Each approach was applied separately to three types of mobile robots to evaluate the performance of each system in responding to changing road conditions, such as bends, inclines, descents, and low-light areas. This controller system integrates light sensors, MPU6050, line sensors, and ultrasonic sensors to detect road conditions in real-time. In the first mobile robot, Arduino was used as the main controller which managed the system response based on simple programming. In the second mobile robot, ESP32 is used for adaptive headlights and line followers taking advantage of the advantages of wireless communication and the ability to process sensor data more quickly. Meanwhile, the Raspberry Pi on the third mobile robot functions as the main controller by implementing adaptive headlights and line following using servo steering to increase the accuracy and efficiency of the system. The test results show that the Raspberry Pi-based system has the best performance with a level of detection accuracy. The Raspberry Pi proved superior for adaptive headlight control systems in complex conditions, while the Arduino and ESP32 were better suited for applications with low to medium computing requirements. This research provides deep insight into the effectiveness of different hardware in the development of adaptive lighting systems in mobile robots and can serve as a guide for future robotics applications.