Abstract
Precision aerial delivery of free-falling objects remains a critical challenge for unmanned aerial vehicles (UAVs), particularly in dynamic wind conditions. Traditional methods—relying on manual control or active stabilization mechanisms—often struggle with accuracy and adaptability. While recent advances in data-driven control systems suggest the potential of deep learning for ballistic prediction, existing approaches either require complex hardware (e.g., guided payloads) or lack robustness to environmental disturbances. This study introduces an AI-powered ballistic assistant that enhances free-fall accuracy through real-time integration into drone position control. By leveraging a deep neural network (DNN) trained on extensive physics-based simulations—covering diverse wind conditions and release altitudes—the system predicts relative target distance and optimizes release points autonomously. Experimental results demonstrate the drone with the integrated AI assistant achieves significantly improved accuracy compared to manual release. At altitudes up to 200 meters, the system maintains 89.9% accuracy, gradually declining to 10% at 400 meters due to increasing environmental variability. Across 1700 test episodes at varying altitudes, the AI -assisted approach achieves a 76.80 % overall landing accuracy, outperforming manual control in the same simulation environment.