# Multi-UAV Area Coverage Track Planning Based on the Voronoi Graph and Attention Mechanism
## 3 Method
### Basic Trajectory Optimization Algorithm  
The flight path planning of UAV cooperative operation belongs to the category of full coverage flight path planning. The problem is described as follows: given several UAVs and a bounded operation area to be covered, the flight starting point and direction of UAVs are set, and the covered flight is carried out. Under the premise of not missing the operation area, the repetitive covering path is less, the effective flight path is longer, and the energy loss is less. In order to facilitate the research, the problem is simplified reasonably. The terrain of the area to be covered is flat, regardless of the relief changes. The height of the UAV is unchanged during operation, that is, the fixed height flight. For a single drone and the area it is responsible for covering, we plan to use the classic network in deep learning, the ResNet convolutional neural network, to select and optimize the track. ResNet, a full-name residual network, is a deep convolutional neural network (CNN) architecture originally proposed by [PERSON] et al. in the 2015 paper \"Deep Residual Learning for Image Recognition\". This architecture has made revolutionary progress in solving the problem of deep neural network training, especially when dealing with intense networks; it can effectively avoid the problem of gradient vanishing or gradient explosion, allowing the network to improve accuracy by adding more layers without falling into the dilemma of performance degradation. The core idea of ResNet is to introduce residual learning. In traditional deep networks, features are directly transmitted between layers. At the same time, ResNet adds \"skip connections\" (also called \"short-circuit connections\" or \"identity mapping\") so that the output of a particular layer is not only passed to the next layer but may jump several layers before passing. This means that when the network learns, it does not directly learn the output of each layer but learns the residual between the input and output, that is, the difference between the target mapping and the input. If the network layers can copy the input directly to the output, these layers can learn an identity mapping that keeps the input unchanged. This way, even if the network is deep, the gradient can be propagated more efficiently through these direct connections. ResNet has many variants. Depending on the number of layers, the common ones are ResNet-18, ResNet-34, ResNet-50, ResNet-101, and ResNet-152. Considering both performance and speed, this article uses the ResNet-50 model, which introduces a \"bottleneck\" design. Each residual unit contains three layers of convolution (1 \(\times\) 1, 3 \(\times\) 3, 1 \(\times\) 1 convolution), where 1 \(\times\) 1 convolution is used for dimensionality reduction and dimensionality increase, and 3 \(\times\) 3 convolution is responsible for processing features. This design reduces parameters while maintaining network performance. The introduction of ResNet has extensively promoted the development of deep learning, and it has set new performance standards in many tasks, such as image classification, object detection, and semantic segmentation. The design concept of ResNet also inspired the development of many subsequent network architectures, such as DenseNet and MobileNet. These networks further optimized the computational efficiency and the number of parameters while maintaining or improving performance. ResNet solves the problem of difficult deep network training through its innovative residual learning mechanism, enabling the network to improve its learning ability by deepening the network depth without falling into overfittingand opening the door for deep learning to develop deeper networks. ResNet achieved excellent performance in multiple computer vision tasks and provided valuable inspiration for the design of subsequent deep learning models. Its influence runs through all aspects of deep learning and has become a milestone in the history of deep learning.