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The robot Detection  lane while avoiding obstacles using Arduino and Raspberry Pi. Part of Kuwait University, Robotics class with Dr. Alshaibani.\n\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"Photos/car3.jpg\" alt=\"Car 3\" width=\"45%\"\u003e\n  \u003cimg src=\"Photos/car4.jpg\" alt=\"Car 4\" width=\"45%\"\u003e\n\u003c/p\u003e\n\n### Project Phases:\n1. **Phase 1:** Assembly and remote operation.\n2. **Phase 2:** Autonomous driving with obstacle avoidance.\n\n## Key Features\n- **Remote Control:** Manual operation via a web interface (Flask server on Raspberry Pi).\n- **Autonomous Navigation:** Utilizes Q-learning for path-following and obstacle avoidance.\n- **Sensor Integration:**\n  - 3x IR Sensors for line detection.\n  - 3x Ultrasonic Sensors for obstacle detection.\n- **Motor Control:** L298N motor driver with 4 DC motors.\n- **Communication:** UART serial between Raspberry Pi (main controller) and Arduino (motor/sensor handler).\n\n## Components\n\n### **Hardware**\n- Raspberry Pi 4 (Main controller, runs Flask server).\n- Arduino Uno (Handles motor control \u0026 sensor readings).\n- L298N Motor Driver (Controls DC motors).\n- 4x DC Motors with Wheels (Movement).\n- 3x IR Infrared Sensors (Line detection).\n- 3x Ultrasonic Sensors (HCSR04) (Obstacle detection).\n- Power Bank \u0026 Li-ion Battery (Power supply).\n\n### **Software \u0026 Tools**\n- Python (Flask server, Q-learning algorithm).\n- Arduino IDE (Motor/sensor control logic).\n- GitHub (Version control).\n\n## Phase 1: Remote Control\n\n### **Assembly \u0026 Operation**\n- Flask web server allows manual control (forward, backward, left, right).\n- Commands sent via UART to Arduino, which drives motors.\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"Photos/pahse1-website.png\" alt=\"Phase 1 Website\" width=\"30%\"\u003e\n\u003c/p\u003e\n\n## Phase 2: Autonomous Mode\n\n### **Q-Learning Algorithm**\n- **States:** IR sensor readings (e.g., `000` = all white, `010` = middle black).\n- **Actions:** Forward, left, right, scan_left, scan_right, strongLeft, strongRight.\n- **Rewards:** Predefined based on sensor inputs (e.g., staying centered = high reward).\n- **Training:** Robot learns optimal path-following via reinforcement learning.\n\n### **Obstacle Avoidance**\n- Ultrasonic sensors detect obstacles (threshold: 20cm).\n- If an obstacle is detected, the robot switches lanes and signals with LEDs.\n  \n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"Photos/phase2-website.jpg\" alt=\"Phase 2 Website\" width=\"30%\"\u003e\n\u003c/p\u003e\n\n## Setup \u0026 Usage\n\n### **Setup Guide**\n#### **Hardware Assembly**\n1. Connect motors, sensors, and controllers as per the wiring diagram.\n2. Power Raspberry Pi (USB-C) and Arduino (Li-ion battery).\n\n#### **Software Setup**\n1. Upload Arduino code to handle sensors/motors.\n2. Run Flask server on Raspberry Pi (`app.py`) for web interface.\n\n#### **Training Autonomous Mode**\n1. Start training via the web interface.\n2. Robot explores and updates Q-table (`model.pth`).\n\n## Demo\n\n### **Robot Demo**\n- Place robot on a track with lanes and obstacles.\n- Switch between manual and autonomous modes.\n\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"Photos/maze.jpg\" alt=\"Maze\" width=\"30%\"\u003e\n\u003c/p\u003e\n\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"Photos/car1.jpg\" alt=\"Car 1\" width=\"45%\"\u003e\n  \u003cimg src=\"Photos/car2.jpg\" alt=\"Car 2\" width=\"30%\"\u003e\n\u003c/p\u003e\n\n## Challenges \u0026 Improvements\n\n### **Challenges**\n- Sensor calibration for varying light conditions.\n- Motor synchronization issues.\n- Q-learning convergence time.\n\n### **Future Work**\n- Fix Issues with Q-learning\n- Add a camera for advanced object recognition.\n\n## Contributors\n- **Hala Almutairi**\n- **Zaharaa Alrashidi**\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhalacoded%2Fsmart-car-robot","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhalacoded%2Fsmart-car-robot","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhalacoded%2Fsmart-car-robot/lists"}