Artificial Intelligence-Based Control Strategies for Unmanned Aerial Vehicles

Drones, also known as Unmanned Aerial Vehicles, or UAVs, are becoming more and more well-liked as a multifunctional tool that can be used in a variety of industries, including environmental monitoring, search and rescue, agricultural, and surveillance. In this work, we explore the potential for significantly increased UAV efficiency, versatility, and durability with AI-based control techniques. The research technique combines simulation, experimental validation, and mathematical modelling to fully investigate AI-based control systems. We develop a mathematical model of UAV dynamics and use a deep reinforcement learning (DRL) technique based on Proximal Policy Optimisation (PPO) to control the drones. Both theoretical analysis and real-world testing on a DJI Matrice 210 RTK platform validate the method's efficacy. The algorithm's capacity to provide precise and accurate answers is shown by the average location error's steady decline over time. It is very resilient, remaining stable in the face of wind gusts, and remarkably flexible, with quick response times. These patterns are shown graphically. According to the study's findings, AI-based control algorithms might greatly advance UAV technology by increasing its precision, adaptability, and dependability. Future priorities will include advanced machine learning techniques, multi-agent systems, safety measures, ethical frameworks, human-AI collaboration, environmental impact assessments, and urban integration. Through the creation of new capabilities and the resolution of urgent concerns, these activities have significant potential to impact UAV development in the future.