## 4 Experimental Phase and Analysis
Likewise, the AED model maintained a similar behavior in its two variants, not affecting the word size. In this way, this approach is the most suitable solution for path generation compared to scenarios involving more nodes. Finally, the HC model had an acceptable behavior, but this model requires an increase in the number of samples in the training set to cover more possibilities and increases the development time. Table 6 describes the performance when implemented on embedded devices, such as the Jetson nano 2G and the Android device, Moto X4, with a 630 GPU and a CPU with 2 GHz. The Jetson nano 2G can execute the model using the specific Nvidia technology, RTTensor, and only the CPU. Similarly, the Android device can also execute the model using a CPU, a GPU, and the API developed by Google called NN-API. According to [60], when a system achieves at least 10 FPS, it is considered a real-time system. Thus, the evaluation showed that the performance took advantage of the Nvidia technology, as the RTTensors, with Float16. Further, the Android device also executed on the GPU and the reduced size of the floating point. Unfortunately, the NN-API gave an inadequate performance since this Android device is limited compared to high-performance Android devices. The models presented in this paper can be improved by increasing the number of layers or decreasing the number of nodes and implementing strategies that enhance the behavior for each approach. Figure 24 shows the path generated by this proposal. We implemented an augmented reality system on a mobile device to illustrate the path. In this Figure 24a,b, a successful \begin{table}
\begin{tabular}{c c c} \hline \hline
**Device** & **Float16** & **Florata32** \\  & **(FPS)** & **(FPS)** \\ \hline Jetson nano 2G Tensorflow-lite & 11 & 10 \\ Jetson nano 2G Tensor RT & 41 & 10 \\ Android device Moto X4 CPU-4 threads & 14 & 12 \\ Android device Moto X4 GPU & 20 & 16 \\ Android device Moto X4 NN-API & 6 & 6 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Frames per second on embedded devices. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Size** & **Accuracy** & **Accuracy** & **Accuracy** & **Coefficient** \\  & **of** & **Euclidean** & **Manhattan** & **Cosine** & **Free** \\  & **Word** & **Distance** & **Distance** & **Similarity** & **Collision \(\uparrow\)** \\  & **(mean-std) \(\downarrow\) & **(mean-std) \(\downarrow\)** & **(mean-std) \(\downarrow\)** & **(mean-std) \(\downarrow\)** & **Collision \(\uparrow\)** \\ \hline \multirow{2}{*}{DCNN-Full} & Float16 & 27.9917 \(\pm\) 2.1647 & 107.1066 \(\pm\) 7.3813 & 0.9358 \(\pm\) 0.2028 & 0.68 \\ \cline{2-5}  & Float32 & 27.9910 \(\pm\) 2.1641 & 107.1053 \(\pm\) 7.3780 & 0.9358 \(\pm\) 0.2028 & 0.72 \\ \hline \multirow{2}{*}{DCNN-MNav} & Float16 & 10.1415 \(\pm\) 1.5896 & 28.7415 \(\pm\) 2.4785 & 0.9748 \(\pm\) 0.01811 & 0.76 \\ \cline{2-5}  & Float32 & 10.1413 \(\pm\) 1.5892 & 28.7408 \(\pm\) 2.4779 & 0.9749 \(\pm\) 0.01814 & 0.78 \\ \hline \multirow{2}{*}{HC-Full} & Float16 & 12.6325 \(\pm\) 1.6896 & 19.1415 \(\pm\) 3.1415 & 0.9813 \(\pm\) 0.01258 & 0.92 \\ \cline{2-5}  & Float32 & 12.6319 \(\pm\) 1.6888 & 19.1410 \(\pm\) 3.1404 & 0.9814 \(\pm\) 0.01256 & 0.92 \\ \hline \multirow{2}{*}{HC-MNav} & Float16 & 5.21227 \(\pm\) 1.9859 & 10.0046 \(\pm\) 1.0156 & 0.9916 \(\pm\) 0.0009 & 0.94 \\ \cline{2-5}  & Float32 & 5.21223 \(\pm\) 1.9853 & 10.0038 \(\pm\) 1.0149 & 0.9916 \(\pm\) 0.0011 & 0.94 \\ \hline \multirow{2}{*}{AED-Full} & Float16 & 17.1215 \(\pm\) 2.4475 & 38.8528 \(\pm\) 2.2332 & 0.9415 \(\pm\) 0.0154 & 0.82 \\ \cline{2-5}  & Float32 & 17.1208 \(\pm\) 2.4470 & 38.8520 \(\pm\) 2.2325 & 0.9416 \(\pm\) 0.0152 & 0.84 \\ \hline \multirow{2}{*}{AED-MNav} & Float16 & 4.7485 \(\pm\) 0.9869 & 7.4415 \(\pm\) 1.2023 & 0.9814 \(\pm\) 0.0224 & 0.88 \\ \cline{2-5}  & Float32 & 4.7478 \(\pm\) 0.9862 & 7.4409 \(\pm\) 1.2018 & 0.9814 \(\pm\) 0.0224 & 0.88 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance of the transfer-learning approach and its standard deviation in 50 samples.