TY - GEN

T1 - UAV Trajectory Planning for Efficient Data Collection in Large-Scale Monitoring

AU - Matkurbanov, Tulkin

AU - Rodionov, Alexey

AU - Mengliev, Davlatyor

N1 - Conference code: 21

PY - 2025/11/5

Y1 - 2025/11/5

N2 - This article explores methodologies for constructing an efficient UAV flight trajectory to collect data from sensor nodes deployed across large-scale monitoring areas. The proposed approach seeks to minimize the overall flight path length while ensuring complete coverage of all sensor zones, taking into account the spatial coordinates and coverage radius of each sensor. Three distinct trajectory planning algorithms are developed and analyzed in this study: the three-point method, the tangential movement method, and the greedy coverage method. Each method is implemented as an independent algorithm, consisting of three main stages: initial route construction, geometric refinement, and coverage validation. All algorithms were evaluated on a common dataset of sensor locations under identical experimental conditions. The evaluation metrics include total trajectory length, number of maneuver points (waypoints), and computational time. The results provide a comparative analysis of each method's performance in terms of efficiency, scalability, and route optimization potential. In conclusion, the study presents a comprehensive framework for UAV trajectory planning by integrating geometric, algorithmic, and computational considerations. Based on the spatial distribution and density of sensor nodes, practical recommendations are offered for selecting the most appropriate trajectory planning strategy for large-scale data collection tasks.

AB - This article explores methodologies for constructing an efficient UAV flight trajectory to collect data from sensor nodes deployed across large-scale monitoring areas. The proposed approach seeks to minimize the overall flight path length while ensuring complete coverage of all sensor zones, taking into account the spatial coordinates and coverage radius of each sensor. Three distinct trajectory planning algorithms are developed and analyzed in this study: the three-point method, the tangential movement method, and the greedy coverage method. Each method is implemented as an independent algorithm, consisting of three main stages: initial route construction, geometric refinement, and coverage validation. All algorithms were evaluated on a common dataset of sensor locations under identical experimental conditions. The evaluation metrics include total trajectory length, number of maneuver points (waypoints), and computational time. The results provide a comparative analysis of each method's performance in terms of efficiency, scalability, and route optimization potential. In conclusion, the study presents a comprehensive framework for UAV trajectory planning by integrating geometric, algorithmic, and computational considerations. Based on the spatial distribution and density of sensor nodes, practical recommendations are offered for selecting the most appropriate trajectory planning strategy for large-scale data collection tasks.

KW - Trajectory planning

KW - Simulation

KW - Scalability

KW - Data collection

KW - Traveling salesman problems

KW - Autonomous aerial vehicles

KW - Trajectory

KW - Topology

KW - Optimization

KW - Monitoring

KW - unmanned aerial vehicle

KW - trajectory planning

KW - traveling salesman problem with neighborhoods

KW - optimization algorithm

KW - sensor network

KW - data collection

UR - https://www.scopus.com/pages/publications/105023655659

UR - https://www.mendeley.com/catalogue/015400ae-85c5-3905-a879-7cc9b2ec4c12/

U2 - 10.1109/opcs67346.2025.11219373

DO - 10.1109/opcs67346.2025.11219373

M3 - Conference contribution

SN - 979-8-3315-8982-0

SP - 1

EP - 5

BT - Proceedings - 2025 21st International Asian School-Seminar on Optimization Problems of Complex Systems (OPCS)

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 21st International Asian School-Seminar on Optimization Problems of Complex Systems (OPCS)

Y2 - 7 July 2025 through 17 July 2025

ER -