Optimizing Release Points for Precise Payload Delivery by UAVs Under Wind Uncertainty: A Knowledge‐Based Approach Using Differential Evolution

ABSTRACT Windy conditions challenge precise payload delivery by unmanned aerial vehicle (UAV), but wind variability defined within lower and upper bounds at any instance can assist to optimize candidate release points. Therefore, at first, it is crucial to collect candidate release points caused by wind variability. In this paper, knowledge of candidate release points is drawn by applying ballistic equation. The knowledge about the points then initializes differential evolution (DE) optimization to search an optimum payload release point. Therefore, the proposed method is named as DE with knowledge‐based initialization (KI), that is, DE‐KI. Simulations demonstrate DE‐KI's effectiveness by measuring landing error as root mean square error (RMSE) and achieve an average reduction in RMSE compared to existing methods. For instance, DE‐KI outperforms two other alternative approaches by an average RMSE of and with varying payload weight, and and with varying wind speed.