Autonomous Mobile Robot for Obstacle Avoidance and Traffic Light Detection
RoboGuideX is an Arduino-based autonomous mobile robot designed to navigate safely in dynamic environments. It integrates multiple sensors for real-time obstacle avoidance and traffic light recognition, enabling smooth and intelligent movement with minimal human intervention.
✅ Obstacle Detection & Avoidance – Uses an ultrasonic sensor to detect and navigate around obstacles.
✅ Traffic Light Recognition – A TCS230 color sensor identifies red traffic lights and stops accordingly.
✅ Smart Navigation System – Prioritizes stopping at traffic lights while efficiently avoiding obstacles.
✅ DC Motor Control – Uses an L298N motor driver for precise movement control.
✅ Arduino-Based Implementation – Powered by an Arduino Uno for efficient processing.
- Arduino Uno R3 – Main controller for decision-making.
- HC-SR04 Ultrasonic Sensor – Detects obstacles in the path.
- TCS230 Color Sensor – Recognizes traffic light colors.
- L298N Motor Driver – Controls motor movement.
- DC Motors with Gearbox – Enables movement and navigation.
- SG90 Servo Motor – Adjusts the sensor’s angle for better obstacle detection.
- Power Supply (Batteries) – Provides energy for the system.
- 2-Wheel Robot Chassis – Physical base for the robot.
-
Obstacle Avoidance:
- The ultrasonic sensor continuously scans for objects in front of the robot.
- If an obstacle is detected, the robot decides whether to move left, right, or stop.
-
Traffic Light Detection:
- The color sensor checks for red lights.
- If a red signal is detected, the robot halts until it turns green.
-
Movement & Decision Making:
- The robot prioritizes stopping at red lights over obstacle avoidance.
- If no traffic signal is detected, it continues moving while avoiding obstacles.
git clone https://github.com/Nadazeineedin/RoboGuideX.git
cd RoboGuideX
- Open the Arduino IDE.
- Connect the Arduino Uno via USB.
- Upload the provided Arduino C code (
RoboGuideX.ino
).
- Connect the ultrasonic and color sensors to the Arduino.
- Wire the L298N motor driver to the motors.
- Secure the components onto the chassis.
- Insert the batteries and power on the system.
- Place obstacles and traffic signals in its path to test functionality.
🔹 Implement machine learning for smarter navigation.
🔹 Enhance traffic signal recognition with more color detection.
🔹 Integrate GPS and Bluetooth for remote control and tracking.
🔹 Use LiDAR sensors for more precise obstacle detection.
This project is licensed under the MIT License – you are free to modify and distribute it.
![]() Zuhour Alsaqa |
![]() KhaledOMY |
💡 Contributions are welcome! Feel free to submit issues or pull requests.
📧 For inquiries, reach out to [nadazeineddin29@gmail.com].