## 4 Experimental Phase and Analysis
}}{N_{samples}} \tag{15}\]  
The three models with one-to-one linking had high randomization. Therefore, it was essential to connect both realities with the GAN to reduce that random behavior. In this experiment, each node was composed of two points of the output. For example, if the output vector had ten values, this means that the path had five nodes. The results show that the solution with the navigation mesh improved the performance because the number of nodes was fewer than the full path. Besides, the model with the best behavior was the hierarchical clustering because this model is associated with the sample existing in the training samples, but samples in the dataset generate the path. Therefore, this approach was the best with respect to the free collision coefficient. However, all possible solutions must appear in the dataset to improve the accuracy compared with the auto-encoder. On the contrary, the deep convolutional neural network had a competitive behavior, but the error increased, while the path was long. Finally, the auto-encoder model predicted the sequence; hence, this approach reduced the number of training samples more than hierarchical clustering. Figure 22: Auto-encoder behavior with navigation meshes and the full path. Concerning the execution on embedded devices through a model with fewer operations, Figure 23 shows that the performance of 1000 epochs for the optimized network for mobile devices using a transfer-learning approach had a precision of 95% with 20 test samples. In order to validate the accuracy, the evaluation of the metrics was repeated, including two models with different floats generated by Tensorflow-lite because the GPU's performance increases with a short float size. Table 5 describes the performance of the test evaluation vectors on different models with full nodes and the navigation mesh with both sizes of float points in 16 bit and 32 bit, respectively. The main difference between the two types of word size is the free path coefficient because the Float16 word size differs slightly by one node. This behavior was due to a decrease in the word size precision in the operations. However, the model that affected this behavior the most was the DCNN-Full model since it has morenodes, and the weights change their behavior due to the word size differences. Another factor that affects its behavior is that the normalized output is multiplied by a constant, which caused the error to increase. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Accuracy** & **Accuracy** & **Accuracy** & **Coefficient** \\  & **Euclidean** & **Manhattan** & **Cosine** & **Free** \\  & **Distance** & **Distance** & **Similarity** & **Collision**\(\uparrow\) \\  & **(mean-std)**\(\downarrow\) & **(mean-std)**\(\downarrow\) & **(mean-std)**\(\downarrow\) & **Collision**\(\uparrow\) \\ \hline DCNN-one2 one & 53.8667\(\pm\) 3.9055 & 128.8 \(\pm\) 14.5931 & 0.7069 \(\pm\) 0.0586 & 0.02 \\ \hline DCNN-Full & 22.6847 \(\pm\) 3.5832 & 86.7401 \(\pm\) 6.5722 & 0.8996 \(\pm\) 0.03491 & 0.66 \\ \hline DCNN-MNav & 8.4568 \(\pm\) 1.5475 & 22.1415 \(\pm\) 4.2568 & 0.91454 \(\pm\) 0.0355 & 0.84 \\ \hline HC-one2 one & 15.1254 \(\pm\) 2.4585 & 32.1346 \(\pm\) 3.1354 & 0.8745 \(\pm\) 0.02499 & 0.82 \\ \hline HC-Full & 10.1411 \(\pm\) 4.2164 & 24.2668 \(\pm\) 3.1465 & 0.9224 \(\pm\) 0.02587 & 0.92 \\ \hline HC-MNav & 4.2145\(\pm\) 1.2441 & 9.2154 \(\pm\) 1.8795 & 0.9952 \(\pm\) 0.001256 & 0.96 \\ \hline AED-one2 one & 41.6589 \(\pm\) 2.6425 & 130.0512 \(\pm\) 2.9717 & 0.7496 \(\pm\) 0.2654 & 0.04 \\ \hline AED-Full & 19.6318 \(\pm\) 2.1653 & 49.7123 \(\pm\) 9.5722 & 0.9496 \(\pm\) 0.0211 & 0.82 \\ \hline AED-MNav & 4.7855 \(\pm\) 2.1412 & 7.6569 \(\pm\) 3.1336 & 0.98924 \(\pm\) 0.01989 & 0.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance of the end-to-end approach and its standard deviation in 50 samples. \(\downarrow\), lower is better; \(\uparrow\), higher is better. Figure 23: Behavior of the transfer-learning training.