Machine Learning for Adaptive Flight Path Optimization in UAVs

Abstract

Unmanned Aerial Vehicles (UAVs) are increasingly being used in various sectors, from surveillance to delivery services, where flight path optimization plays a critical role in enhancing operational efficiency. Traditional flight path planning methods often rely on pre-defined routes or fixed algorithms, which may not be adaptable to dynamic environmental conditions. This research explores the use of Machine Learning (ML) techniques for adaptive flight path optimization in UAVs, focusing on the ability to adjust in real-time to factors such as weather conditions, airspace congestion, and unexpected obstacles. The study proposes an adaptive framework that integrates reinforcement learning (RL) and deep learning models to enable UAVs to learn and adapt their flight paths based on live data. By using a data-driven approach, the UAVs can make real-time decisions that improve safety, energy efficiency, and mission success rates. The framework incorporates real-time feedback from environmental sensors, UAV performance data, and external systems like air traffic control, allowing the UAVs to dynamically adjust their routes while minimizing energy consumption and maximizing delivery speed. The proposed system was tested through simulations under various scenarios, demonstrating its effectiveness in adapting to changing conditions and optimizing flight paths for improved overall mission performance. This work highlights the potential of ML to revolutionize UAV operations, offering a more intelligent, flexible approach to flight path planning that goes beyond conventional algorithms. The results suggest a significant advancement in autonomous UAV navigation, contributing to more efficient and resilient UAV missions.