A Comprehensive Study of Artificial Intelligence Applications of Drone

Artificial intelligence (AI) has impacted unmanned aerial vehicles (UAVs). Thus, allowing them to perform difficult tasks like domains such as agriculture, logistics, disaster relief, and surveillance with little human intervention. Drones can now carry out many processes such as data collection of data in real time, optimize flight paths, detect objects, and make key decisions on their own, by incorporating novel technologies like Computer Vision (CV), Machine Learning (ML), and Deep Learning (DL). This chapter delves into significant AI applications in UAVs, emphasizing advancements such as flight path optimization via Genetic Algorithms and Neural Networks. It also uses real-world case studies and simulations to investigate advances in energy efficiency, obstacle avoidance, and mission flexibility. Real-time decision-making algorithms use sensor data to adjust drones to changing conditions. On the other hand, DL models like Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs) improve accuracy in tasks such as object detection, categorization, and image analysis. Recent technologies, such as swarm intelligence and multi-agent systems, show how drones can interact autonomously to push the limits of their capabilities. Reinforcement Learning (RL) plays an important role in optimizing work allocation, and AI-driven predictive maintenance and problem detection ensure that UAVs operate consistently. AI-powered technologies improve the management of energy, real-time tracking, and picture recognition, thereby increasing drone efficiency. Finally, the computing power challenges, AI model scalability, and future research areas such as collaborative learning and adapting to unpredictable weather along with navigating regulatory framework are discussed. All of these demonstrate how AI-enabled drones have the potential to reshape industries and drive societal progress.