Project Title:Autonomous Drone Navigation Using Machine Learning

Objective:

To develop a machine learning-based system that enables a drone to navigate through environments autonomously, avoiding obstacles and reaching target destinations safely.

Summary:

This project focuses on teaching a drone how to fly by itself using machine learning techniques. The idea is to make the drone understand its surroundings and make decisions—like turning, moving forward, or stopping—based on data from sensors or cameras.

The project can use supervised learning (for object detection and path following) or reinforcement learning (where the drone learns through trial and error). A simulator (like AirSim or Gazebo) is often used for training before deploying to a real drone to avoid crashes.

Key tasks include path planning, obstacle detection, and decision-making. Deep learning models like CNNs can be used for image processing, while RL algorithms like Deep Q-Learning or PPO help in movement decision-making.

Key Steps:

Set Up Simulation/Drone Environment – Use tools like AirSim, Gazebo, or a real drone.

Collect Data – Use camera images, LiDAR, or sensor data.

Train Models – For navigation, obstacle detection, and path planning.

Test and Refine – Run trials and improve the model's decision-making.

Technologies Used:

Python

TensorFlow / PyTorch

ROS (Robot Operating System)

AirSim or Gazebo (simulators)

OpenCV for image processing

Applications:

Delivery drones (e.g., medical or food delivery)

Disaster zone navigation

Surveillance and security

Smart agriculture and inspection tasks

Expected Outcomes:

A trained model that helps a drone navigate without human control

Simulation videos or real-world demos

Performance metrics like success rate, collision avoidance, and path efficiency