AI Powered Smart Car

The growing accessibility of open-source hardware and embedded programming has enabled the development of lowcost autonomous robotic systems for education and research. This project presents the design and implementation of amultifunctional DIY self-driving car using the Arduino Uno microcontroller and ESP32 Bluetooth module, combined withvarious sensors and modules to achieve intelligent autonomous behavior. The vehicle integrates multiple navigation andinteraction features including line following, obstacle avoidance with pathfinding, GPS-based outdoor navigation, Bluetoothremote control, and offline voice command execution.At the hardware level, the system utilizes a 4-wheel drive chassis powered by a lithium-polymer (Li-Po) battery. Keycomponents include a US-100 ultrasonic distance sensor mounted on a servo motor for 180-degree scanning, an IR sensor arrayfor line tracking, a GPS module (NEO-6M) for real-time geolocation, and an L298N motor driver for motor control. The ESP32module handles Bluetooth-based mobile app control and interprets voice commands using a standalone offline recognitionmodule. The software is developed using the Arduino IDE and leverages libraries such as Servo.h, SoftwareSerial, TinyGPS++,and PID_v1 to manage various control mechanisms.The significance of this research lies in its integration of multiple autonomous and interactive features into a compact and costeffective robotic platform. Unlike previous DIY car models that were limited to basic obstacle avoidance or indoor use, thissystem offers outdoor mobility, modular control modes, and extended runtime. Furthermore, its educational value is substantial,making it a practical tool for robotics, automation, and embedded systems training. The modularity and scalability of the designalso allow for future upgrades such as machine learning, camera-based perception, IoT connectivity, and advanced pathplanning.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-05-08
DOI
https://doi.org/10.5281/zenodo.15361816
Primary Topic
IoT-based Smart Home Systems
Type
article
Field-Weighted Citation Impact
0.00
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article

AI Powered Smart Car

Zenodo (CERN European Organization for Nuclear Research)
IoT-based Smart Home Systems
article

AI Powered Smart Car

article en

Abstract

The growing accessibility of open-source hardware and embedded programming has enabled the development of lowcost autonomous robotic systems for education and research. This project presents the design and implementation of amultifunctional DIY self-driving car using the Arduino Uno microcontroller and ESP32 Bluetooth module, combined withvarious sensors and modules to achieve intelligent autonomous behavior. The vehicle integrates multiple navigation andinteraction features including line following, obstacle avoidance with pathfinding, GPS-based outdoor navigation, Bluetoothremote control, and offline voice command execution.At the hardware level, the system utilizes a 4-wheel drive chassis powered by a lithium-polymer (Li-Po) battery. Keycomponents include a US-100 ultrasonic distance sensor mounted on a servo motor for 180-degree scanning, an IR sensor arrayfor line tracking, a GPS module (NEO-6M) for real-time geolocation, and an L298N motor driver for motor control. The ESP32module handles Bluetooth-based mobile app control and interprets voice commands using a standalone offline recognitionmodule. The software is developed using the Arduino IDE and leverages libraries such as Servo.h, SoftwareSerial, TinyGPS++,and PID_v1 to manage various control mechanisms.The significance of this research lies in its integration of multiple autonomous and interactive features into a compact and costeffective robotic platform. Unlike previous DIY car models that were limited to basic obstacle avoidance or indoor use, thissystem offers outdoor mobility, modular control modes, and extended runtime. Furthermore, its educational value is substantial,making it a practical tool for robotics, automation, and embedded systems training. The modularity and scalability of the designalso allow for future upgrades such as machine learning, camera-based perception, IoT connectivity, and advanced pathplanning.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 55%
IoT-based Smart Home Systems
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