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.
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Obstacle Detection & Avoidance β Uses an ultrasonic sensor to detect and navigate around obstacles.
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Traffic Light Recognition β A TCS230 color sensor identifies red traffic lights and stops accordingly.
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Smart Navigation System β Prioritizes stopping at traffic lights while efficiently avoiding obstacles.
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DC Motor Control β Uses an L298N motor driver for precise movement control.
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Arduino-Based Implementation β Powered by an Arduino Uno for efficient processing.
- Arduino Uno R3 β Main controller for decision-making.
- HC-SR04 Ultrasonic Sensor β Detects obstacles in the path.
- TCS230 Color Sensor β Recognizes traffic light colors.
- L298N Motor Driver β Controls motor movement.
- DC Motors with Gearbox β Enables movement and navigation.
- SG90 Servo Motor β Adjusts the sensorβs angle for better obstacle detection.
- Power Supply (Batteries) β Provides energy for the system.
- 2-Wheel Robot Chassis β Physical base for the robot.
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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.
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Traffic Light Detection:
- The color sensor checks for red lights.
- If a red signal is detected, the robot halts until it turns green.
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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.
πΉ Implement machine learning for smarter navigation.
πΉ Enhance traffic signal recognition with more color detection.
πΉ Integrate GPS and Bluetooth for remote control and tracking.
πΉ Use LiDAR sensors for more precise obstacle detection.
This project is licensed under the MIT License β you are free to modify and distribute it.
Zuhour Alsaqa |
KhaledOMY |
π‘ Contributions are welcome! Feel free to submit issues or pull requests.
π§ For inquiries, reach out to [nadazeineddin29@gmail.com].
