3D UAV Trajectory Planning for IoT Data Collection via Matrix-Based Evolutionary Computation

• URL: http://arxiv.org/abs/2410.05759v1
• Date: Tue, 8 Oct 2024 07:33:48 GMT
• Title: 3D UAV Trajectory Planning for IoT Data Collection via Matrix-Based Evolutionary Computation
• Authors: Pei-Fa Sun, Yujae Song, Kang-Yu Gao, Yu-Kai Wang, Changjun Zhou, Sang-Woon Jeon, Jun Zhang,
• Abstract summary: Planning 3D UAV trajectories over a continuous temporal-spatial domain is a computationally intensive problem. We propose a matrix-based differential evolution with constraint (MDECH) algorithm to address non-efficient constrained optimization problems.
• Score: 10.91369146380236
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 three-dimensional (3D) UAV trajectories over a continuous temporal-spatial 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 computation-efficient 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.

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