# Multi-UAV Area Coverage Track Planning Based on the Voronoi Graph and Attention Mechanism
## 3 Method
### Basic Trajectory Optimization Algorithm
A series of convolution, pooling, and activation layer operations is conducted. The specific complete operations are shown in Table 1 below. Table 1 clearly shows the ResNet structures of various sizes, where each layer contains the residual structure shown in Figure 2; that is, after the input variable is operated by the weight layer, the new value is added to the initial input value to obtain the new value as the input of the next layer. This operation cleverly integrates the initial information, making the model more feature-rich and providing higher performance for downstream tasks. The principle of the residual structure can be expressed explicitly as Equation (2). \[x^{\prime}=x+F(x) \tag{2}\]  
where x represents the input of this layer, and \(F(x)\) represents the result after its operation. Ultimately, the output of this layer equals the initial input plus the result of its operation. Figure 2 shows that after a series of layer operations, the final step is a fully connected layer, whose core function is to perform dimensionality mapping of features to obtain the required dimensional information. This is illustrated explicitly in Equation (3). \[y=FC(x) \tag{3}\] \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Layer Name** &
\begin{tabular}{c} **Output** \\ **Size** \\ \end{tabular} & **18-Layer** & **34-Layer** & **50-Layer** & **101-Layer** & **152-Layer** \\ \hline Conv1 & \(112\times 112\) & & & \(7\times 7\), 64, stride 2 \\ \hline \multirow{3}{*}{Conv2\_x} & \multirow{3}{*}{\(56\times 56\)} & \(\begin{bmatrix}3\times 3,\ 64\\ 3\times 3,\ 64\end{bmatrix}\times 2\) & \(\begin{bmatrix}3\times 3,\ 64\\ 3\times 3,\ 64\end{bmatrix}\times 3\) & \(\begin{bmatrix}1\times 1,\ 64\\ 3\times 3,\ 64\end{bmatrix}\times 3\) & \(\begin{bmatrix}1\times 1,\ 64\\ 3\times 3,\ 64\end{bmatrix}\times 3\) & \(\begin{bmatrix}1\times 1,\ 64\\ 3\times 3,\ 64\end{bmatrix}\times 3\) & \(\begin{bmatrix}1\times 3\times 3,\ 64\\ 1\times 1,\ 256\end{bmatrix}\times 3\) \\ \hline Conv3\_x & \(28\times 28\) & \(\begin{bmatrix}3\times 3,\ 128\\ 3\times 3,\ 128\end{bmatrix}\times 2\) & \(\begin{bmatrix}3\times 3,\ 128\\ 3\times 3,\ 128\end{bmatrix}\times 4\) & \(\begin{bmatrix}1\times 1,\ 128\\ 3\times 3,\ 128\\ 1\times 1,\ 512\end{bmatrix}\times 4\) & \(\begin{bmatrix}1\times 1,\ 128\\ 3\times 3,\ 128\\ 1\times 1,\ 512\end{bmatrix}\times 8\) \\ \hline Conv4\_x & \(14\times 14\) & \(\begin{bmatrix}3\times 3,\ 256\\ 3\times 3,\ 256\end{bmatrix}\times 2\) & \(\begin{bmatrix}3\times 3,\ 256\\ 3\times 3,\ 256\end{bmatrix}\times 6\) & \(\begin{bmatrix}1\times 1,\ 256\\ 3\times 3,\ 256\end{bmatrix}\times 6\) & \(\begin{bmatrix}1\times 1,\ 256\\ 3\times 3,\ 256\end{bmatrix}\times 23\) & \(\begin{bmatrix}1\times 1,\ 256\\ 3\times 3,\ 256\end{bmatrix}\times 36\) \\ \hline Conv5\_x & \(7\times 7\) & \(\begin{bmatrix}3\times 3,\ 512\\ 3\times 3,\ 512\end{bmatrix}\times 2\) & \(\begin{bmatrix}3\times 3,\ 512\\ 3\times 3,\ 512\end{bmatrix}\times 3\) & \(\begin{bmatrix}1\times 1,\ 512\\ 3\times 3,\ 512\\ 1\times 1,\ 2048\end{bmatrix}\times 3\) & \(\begin{bmatrix}1\times 1,\ 512\\ 3\times 3,\ 512\\ 1\times 1,\ 2048\end{bmatrix}\times 3\) \\ \hline \multicolumn{3}{c}{\(1\times 1\)} & \multicolumn{3}{c}{Average pool, 1000-d fc, softmax} \\ \hline FLOPs & \(1.8\times 10^{9}\) & \(3.6\times 10^{9}\) & \(3.8\times 10^{9}\) & \(7.6\times 10^{9}\) & \(11.3\times 10^{9}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Specific operation table of various ResNet structures. Figure 2: Schematic diagram of residual structure. where x represents the input to the fully connected layer, and y represents the output of the fully connected layer. Essentially, the computational process involves the weighted summation of trainable parameters, fundamentally the operational mechanism of a multilayer perceptron. In this context, the output dimension is 4, representing the four possible flight states of the drone at the current time step, namely up, down, left, and right.