Targeted Express Delivery Drone (TEDD)

An autonomous delivery system designed to revolutionize last-mile logistics by leveraging advanced robotics and AI technologies.

Key Features

• Real-Time Obstacle Avoidance: Utilizes OpenCV for detecting and navigating around obstacles in dynamic environments
• Reinforcement Learning Optimization: Integrates TensorFlow and OpenAI Gym to train reinforcement learning models that optimize flight paths for efficiency and precision
• Simulation and Testing: Simulates flight dynamics and obstacle scenarios using ROS2 and Gazebo, enabling robust pre-deployment testing
• Hardware Integration: Configured Raspberry Pi 5 modules and camera systems to support low-latency processing
• Autonomy and Scalability: Developed with a focus on scalability, allowing for future enhancements such as multi-drone coordination and extended range capabilities

Project Structure

TEDD/
├── src/
│   ├── obstacle_avoidance/     # OpenCV-based obstacle detection
│   ├── reinforcement_learning/  # RL optimization system
│   ├── navigation/             # Autonomous navigation
│   ├── hardware/              # Raspberry Pi integration
│   └── simulation/            # ROS2/Gazebo simulation
├── config/                    # Configuration files
├── tests/                     # Test suites
├── docs/                      # Documentation
└── requirements.txt           # Python dependencies

Quick Start

1. Install dependencies: pip install -r requirements.txt
2. Run simulation: python src/simulation/run_simulation.py
3. Start obstacle avoidance: python src/obstacle_avoidance/main.py
4. Train RL model: python src/reinforcement_learning/train.py

Requirements

• Python 3.8+
• OpenCV 4.5+
• TensorFlow 2.8+
• ROS2 (for simulation)
• Raspberry Pi 5 (for hardware deployment)