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

Pei-Fa Sun, Yujae Song, Kang-Yu Gao, Yu-Kai Wang, Changjun Zhou, Sang-Woon Jeon, Jun Zhang

TL;DR

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.

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.

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

TL;DR

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.

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.
Paper Structure (29 sections, 59 equations, 10 figures, 4 tables, 1 algorithm)

This paper contains 29 sections, 59 equations, 10 figures, 4 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Problem Formulation
  3. System Model
  4. Energy Consumption Model for UAV Traveling
  5. Channel Model for Communication Task
  6. Air-to-ground channel model
  7. Data requirement for GNs
  8. UAV Trajectory Optimization for Energy Minimization
  9. Optimization for Continuous UAV Trajectory
  10. Trajectory Representation Based on the Bézier Curve
  11. Revised Optimization Problem
  12. Optimization Via Matrix-based DE with Constraint Handle
  13. Matrix-Based DE
  14. Selection under Constraints
  15. Constraints C1, C2, C3, and C10
  16. ...and 14 more sections

Figures (10)

  • Figure 1: 3D UAV trajectory planning for IoT data collection.
  • Figure 2: UAV traveling power study with different vertical speeds and acceleration.
  • Figure 3: Illustration of an exemplary Bézier curve with $6$ control points $\mathbf{p}_{1}={[0,0,0]}$, $\mathbf{p}_{2}={[1,1,1]}$, where $\mathbf{p}_{3}={[2,4,2]}$, $\mathbf{p}_{4}={[3,2,3]}$, $\mathbf{p}_{5}={[4,1,2]}$ and $\mathbf{p}_{6}={[5,4,3]}$.
  • Figure 4: 3D terrain generated by three Gaussian sub-functions.
  • Figure 5: Optimized UAV trajectories with and without 3D terrain.
  • ...and 5 more figures