Model predictive altitude and velocity control in ergodic potential field directed multi-UAV search

This article addresses survey missions involving multiple uncrewed aerial vehicles (UAVs) over complex, varying terrain. The methodology integrates a probabilistic model of target’s position uncertainty with UAV flight dynamics, camera properties, and a machine learning-based detection system. It estimates undetected target probability and overall search performance, feeding into a feedback loop that combines 2-D ergodic search with model predictive control (MPC) of UAV altitude and velocity. Trial trajectory optimization accounts for sensing characteristics and operational constraints, producing terrain-aware, collision-free trajectories that balance area coverage with target detection. Simulations demonstrate the integration of MPC and ergodic search, enabling dynamic altitude adjustments to enhance the search performance. The control algorithm operates in real time and performs reliably under uncertainty. Field experiments provided training data, validated the method, and confirmed compliance with motion constraints. Detection rates closely match model predictions, demonstrating stable performance even under significant deviations from ideal conditions. The framework, thus, offers a reliable solution for autonomous multi-UAV search operations in real-world environments.