Autonomous drone navigation and obstacle avoidance
Table Of Contents
1. Introduction
1.1 Motivation and Objectives
1.2 Applications of Autonomous Drone Navigation
2. Literature Review
2.1 State-of-the-art Drone Navigation Systems
2.2 Computer Vision and Obstacle Detection Techniques
3. Sensor Fusion and Perception
3.1 Integration of Cameras, Lidar, and Inertial Sensors
3.2 Environmental Perception and Situational Awareness
4. Path Planning and Collision Avoidance
4.1 Reactive and Predictive Navigation Algorithms
4.2 Dynamic Obstacle Avoidance Strategies
5. Machine Learning for Autonomous Navigation
5.1 Training Data Collection and Annotation
5.2 Deep Learning Models for Scene Understanding
Thesis Abstract
This project focuses on the development of autonomous navigation and obstacle avoidance systems for drones using computer vision and machine learning techniques. The project aims to enable drones to navigate complex environments, detect and avoid obstacles in real time, and plan collision-free paths. The research will contribute to the advancement of autonomous aerial vehicles for applications such as surveillance, delivery, and infrastructure inspection.
Thesis Overview
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