Project Title:

Autonomous Drone Navigation Using Computer Vision

Project Description:

The Autonomous Drone Navigation Using Computer Vision project involves the development of an intelligent drone system capable of navigating its environment without human intervention. Using real-time video input and advanced computer vision algorithms, the drone identifies obstacles, detects paths, and makes autonomous flight decisions to safely reach a target location.

This project integrates machine learning, image processing, and robotics to enable drones to perform tasks such as object detection, obstacle avoidance, line/path following, and SLAM (Simultaneous Localization and Mapping). It is designed for use in various fields including agriculture, surveillance, disaster management, and delivery systems.

Key Features:

• Obstacle Detection and Avoidance: Uses camera and sensors (e.g., LiDAR or ultrasonic) to detect and avoid obstacles in real time.

• Path and Object Recognition: Identifies specific routes, objects, or markers (e.g., QR codes, colored paths).

• SLAM (Simultaneous Localization and Mapping): Builds a map of an unknown environment while tracking the drone’s location within it.

• Autonomous Flight Control: Makes real-time decisions for navigation, hovering, turning, and landing.

• Return-to-Base (RTB) Feature: Safely returns to the starting point upon task completion or battery low.

Technologies Used:

• Programming Language: Python / C++

• Computer Vision Libraries: OpenCV, TensorFlow, YOLOv8 (for object detection)

• Flight Controller: ArduPilot / PX4

• Drone Platform: Raspberry Pi + DroneKit / DJI SDK

• Sensors: GPS, IMU, Ultrasonic, Camera Module

• SLAM Algorithms: ORB-SLAM / RTAB-Map

Use Cases:

• Monitoring crop health in agriculture using aerial images.

• Search and rescue operations in disaster-hit areas.

• Autonomous delivery in smart cities or rural regions.

• Real-time surveillance of restricted or hazardous zones.

Benefits:

• Reduces the need for manual drone operation.

• Increases precision and efficiency in mission-critical tasks.

• Enhances safety in high-risk environments.

• Promotes innovation in smart mobility and automation sectors.