3D Trajectory Design for UAV Data Collection with Optimized by Matrix-based Evolution Computation

Abstract

UAVs are increasingly becoming vital tools in various wireless communication applications including internet of things (IoT) and sensor networks, thanks to their rapid and agile non-terrestrial mobility. Despite recent research, planning threedimensional (3D) UAV trajectories over a continuous temporalspatial domain remains challenging due to the need to solve computationally intensive optimization problems. In this paper, we study UAV-assisted IoT data collection aimed at minimizing total energy consumption while accounting for the UAV’s physical capabilities, the heterogeneous data demands of IoT nodes, and 3D terrain. We propose a matrix-based differential evolution with constraint handling (MDE-CH), a computationefficient evolutionary algorithm designed to address non-convex constrained optimization problems with several different types of constraints. Numerical evaluations demonstrate that the proposed MDE-CH algorithm provides a continuous 3D temporal–spatial UAV trajectory capable of efficiently minimizing energy consumption under various practical constraints and outperforms the conventional fly–hover–fly model for both two-dimensional (2D) and 3D trajectory planning.