Simulation software for autonomous navigation of UAVS in GPS denied environments

Abstract

In recent years, the rapid evolution of drone technology has revolutionized various industries, ranging from search and rescue operations to disaster management. One of the key advantages of drones is their ability to access hard-to-reach areas quickly and efficiently, providing valuable data and support in critical situations. As technology continues to advance, the potential applications of drones in various fields are only expected to grow. Now, there is a strong dependency on Unmanned Aerial Vehicles (UAV) localization and mapping with the Global Positioning System (GPS), but it faces many challenges like signal loss, etc., so the navigation of the drone becomes limited. To overcome these challenges, researchers are exploring alternative methods, such as computer vision and artificial intelligence, for precise drone navigation. These advancements could potentially enhance the capabilities of drones in various applications, leading to more efficient and effective operations. In the context of this thesis, our aim is to create a model that, based on computer vision techniques, allows the UAV to navigate autonomously in a city environment. By harnessing computer vision techniques, this model aims to enable drones to identify and navigate towards recognizable landmarks or features in their surroundings, offering a robust alternative to GPS-dependent navigation. Through the initial use of the AirSim simulator, it gives us the opportunity to fine-tune the algorithm and models to ensure optimal performance and efficiency. This helps to minimize potential risks and errors that could arise during the actual deployment of the drone in real-world drilling operations. Furthermore, the simulator allows for testing in various scenarios, ensuring the algorithm's adaptability and reliability. This comprehensive approach for testing will ultimately result in a more accurate and dependable autonomous navigation system for UAVs in urban settings.