Optimization Scheduling for Long‐Term Maritime Patrol Mission With Multiple Unmanned Aerial Vehicles

ABSTRACT This paper investigates the long‐term maritime patrol mission conducted by multiple fixed‐wing unmanned aerial vehicles (UAVs). Based on the concept of ‘routine operation’, the task scenario for long‐term maritime patrol mission is defined. Subsequently, a novel optimization scheduling model for the multi‐UAV long‐term maritime patrol mission is established. This model is a nonlinear multi‐objective optimization model. Given the characteristics of this model, it is further linearized to transform the nonlinear model into an equivalent linear multi‐objective 0–1 integer optimization model, facilitating easier solving. Two simulation cases are conducted to validate the proposed model and the solving method. The simulation results demonstrate the effectiveness of the proposed approach.

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Publication Details

Journal
IET Control Theory and Applications
Published
2025-01-01
DOI
https://doi.org/10.1049/cth2.70036
Primary Topic
Robotic Path Planning Algorithms
Type
article
Field-Weighted Citation Impact
0.00
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article

Optimization Scheduling for Long‐Term Maritime Patrol Mission With Multiple Unmanned Aerial Vehicles

Shouxu Zhang, Bing Hui Hou, Weisheng Yan
IET Control Theory and Applications
Robotic Path Planning Algorithms
article

Optimization Scheduling for Long‐Term Maritime Patrol Mission With Multiple Unmanned Aerial Vehicles

Shouxu Zhang, Bing Hui Hou, Weisheng Yan
article en

Abstract

ABSTRACT This paper investigates the long‐term maritime patrol mission conducted by multiple fixed‐wing unmanned aerial vehicles (UAVs). Based on the concept of ‘routine operation’, the task scenario for long‐term maritime patrol mission is defined. Subsequently, a novel optimization scheduling model for the multi‐UAV long‐term maritime patrol mission is established. This model is a nonlinear multi‐objective optimization model. Given the characteristics of this model, it is further linearized to transform the nonlinear model into an equivalent linear multi‐objective 0–1 integer optimization model, facilitating easier solving. Two simulation cases are conducted to validate the proposed model and the solving method. The simulation results demonstrate the effectiveness of the proposed approach.

IET Control Theory and ApplicationsVol. 19(1)
Northwestern Polytechnical University (CN), China XD Group (China) (CN)
Openalex Percentile: Top 92%
Robotic Path Planning Algorithms
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