# Multi-UAV Area Coverage Track Planning Based on the Voronoi Graph and Attention Mechanism
## 1 Introduction  
The rapid advancement in UAV technology has made it indispensable across various sectors, including agriculture, environmental protection, disaster relief, and engineering surveying [1]. As regional coverage tasks become increasingly complex, a single-UAV often needs help to complete detection tasks efficiently. Coordinating multiple UAVs offers a solution by enabling comprehensive data coverage over large areas and extended durations with enhanced robustness. As the deployment of drones increases, optimizing their coordination, trajectory planning, and collision avoidance has become crucial [2; 3; 4; 5]. This paper introduces a novel approach to multi-UAV trajectory planning designed to enhance efficiency and task execution. By aligning with the specific needs of the field, our method thoroughly addresses the unique characteristics of drone tasks and the environmental factors involved in regional coverage. We offer a comprehensive solution that ensures accuracy, safety, and efficiency in multi-UAV trajectory planning. Our research not only aims to improve the overall effectiveness of multi-UAV systems but also supports the broader adoption and investment in UAV technology across various sectors. At present, multi-UAV regional coverage trajectory planning is another important research direction in this field, in addition to path optimization algorithms, dynamicdecision-making, collaborative operation, and improved sensor networks [6]. It aims to use multiple UAVs to achieve adequate coverage and real-time monitoring of specific target areas. In the future, as the application demand for UAV technology gradually expands, research in this field will continue to improve and innovate. The intelligent algorithms for multi-UAV trajectory planning are mainly intelligent optimization and machine learning algorithms, including neural network and reinforcement learning algorithms [7; 8; 9; 10]. Intelligent optimization algorithms are widely used in the field of UAV trajectory planning. Researchers use various intelligent optimization algorithms to solve problems in UAV systems, such as path deployment [11], task allocation [12], and control strategies [13], aiming to find the optimal solution or approximate optimal solution. Intelligent optimization algorithms can not only see the optimal trajectory planning route in a short time but also adjust the trajectory planning in real time according to environmental changes and task requirements, adapt to various mission scenarios, and thus significantly improve the mission execution efficiency of UAVs. However, it has a local optimal solution and cannot guarantee the finding of a global optimal solution, which will inevitably affect the accuracy of trajectory planning results [14]. At the same time, some intelligent optimization algorithms require significant computing resources, or some parameters need to be adjusted. When in a scenario with high requirements for real-time decision-making, it will cause inevitable delays in the application of multiple UAVs. In multi-UAV trajectory planning, a neural network algorithm can be used to predict the optimal route of UAVs under different environmental conditions. It has good learning and adaptability and can adapt to complex and changeable flight scenarios in the real world through a large amount of training data, thereby improving the robustness and safety of path planning [15]. At the same time, when a neural network algorithm is applied to UAV trajectory planning, it inevitably requires a large amount of high-quality training data, and the training process is usually time-consuming, requiring a large number of iterative training to achieve good performance. As an intelligent reinforcement learning agent, the drone gradually learns the optimal flight path planning strategy by observing the state of the environment, selecting strategies, and obtaining rewards or penalties based on feedback from the environment.