Project Title : Autonomous Drone Navigation

Objective:
To build an autonomous drone navigation system that can fly and navigate through environments without human intervention, using machine learning for decision-making and obstacle avoidance.

What It Does:
The drone uses AI to interpret its surroundings (using sensors like cameras, LiDAR, and GPS) and make decisions in real time to navigate safely and efficiently to its destination.

Key Concepts:

Autonomous Navigation: The drone makes decisions without human input, using algorithms and sensor data.

Computer Vision: For detecting obstacles, landmarks, or waypoints.

Reinforcement Learning: For training the drone to optimize its flight path and actions over time.

Steps Involved:

Dataset Collection:

Simulated data using drone simulators like AirSim or Gazebo for environment modeling.

Alternatively, real-world data from drone flights (with sensors like cameras, depth sensors, and GPS).

Use or create datasets for specific tasks like obstacle detection, path planning, or waypoint navigation.

Sensor Integration:

Use sensors like RGB cameras, LiDAR, Ultrasonic sensors, or IMUs (Inertial Measurement Units) to perceive the environment.

Data from sensors is processed to identify obstacles and surroundings.

Preprocessing:

Process sensor data (e.g., camera frames or LiDAR scans) using computer vision techniques.

Normalize and clean up noisy data from sensors.

Model Building:

Reinforcement Learning (RL): Train the drone using RL algorithms (like Deep Q-Learning or PPO) where the drone receives rewards for navigating correctly and avoiding obstacles.

Path Planning: Use algorithms like A*, Dijkstra’s, or Rapidly-exploring Random Tree (RRT) for efficient route calculation.

Computer Vision: Implement Convolutional Neural Networks (CNNs) for object detection or obstacle avoidance (e.g., detecting trees, buildings, or other drones).

Model Evaluation:

Test the model in simulation environments for successful navigation, obstacle avoidance, and efficient route planning.

Measure performance in terms of flight success rate, time efficiency, and safety (i.e., collision avoidance).

Deployment (Optional):

Deploy the trained model onto real drones (e.g., using Raspberry Pi or Nvidia Jetson for edge computing).

Integrate real-time obstacle detection and path planning into drone control systems.

Applications:

Delivery Drones: Autonomous navigation for package delivery.

Surveillance: Autonomous navigation for security or monitoring.

Search and Rescue: Drones navigating complex environments to locate people.

Agricultural Monitoring: Drones navigating over fields to collect data.

Mapping: Autonomous flight for geographic or environmental surveys.

Tools & Technologies:

Languages: Python, C++

Libraries/Frameworks: TensorFlow, Keras, OpenCV, PyTorch, ROS (Robot Operating System), AirSim, Gazebo

Platforms: Nvidia Jetson for real-time inference, ROS for drone control, PX4 for flight stack