A Framework for Fast and Reliable UAV Maritime Search Missions

Abstract

Unmanned Aerial Vehicles (UAVs) demonstrated improved response times and safety in life-saving missions. This paper presents a Model Predictive Control (MPC) framework for autonomous UAV search missions in maritime environments, where the UAV must locate multiple castaways floating on the sea surface after a maritime incident. The approach uses receding horizon optimization to plan trajectories that balance two competing objectives: achieving rapid area coverage at high altitudes versus maintaining reliable target detection at lower altitudes. Target detection relies on Convolutional Neural Networks (CNNs), with detection performance characterized through field experiments that measure True Positive (TP) rates and False Positive (FP) rates across multiple flight altitudes. The MPC framework dynamically adjusts the UAV’s altitude and trajectory based on these altitude dependent detection statistics, enabling mission adaptive behavior that outperforms constant-altitude search patterns. Simulation results demonstrate improved search performance compared to conventional constant-altitude missions. Real-world flight experiments validate the practical applicability of the proposed framework and confirm its effectiveness in realistic maritime search scenarios.