## 3 Proposed Work
### Strategy to Connect an Authentic Environment with Its Virtual Representation  
The following strategy is proposed to connect the virtual and real domain using the GAN, whose features are shown in Figures 15 and 16 for generator and discriminator, respectively. The principal feature of this strategy is the connection between both domains, as shown in Figure 17a, where it is an authentic sample and its respective virtual representation with an associated path. This form of connecting both realities is adequate if the number of samples between both domains is the same. However, when an authentic image is not in the domain to predict the connection with the virtual image, the behavior is inadequate because the randomization is high in a one-to-one connection, as shown in Figure 17b. If the domain changes are implemented, the authentic image does not need to exist with the virtual domain. For this reason, the GAN employs the prediction of the virtual representation, having an approximation using the architecture in Figure 17c. Therefore, the randomness is reduced, as shown in Figure 17d, and the number of authentic samples is limited. Figure 14: Path-planning generator based on the auto-encoder approach. (**a**) Input sample. (**b**) Feature extraction network. (**c**) Generative path network. The domain changes from the real to the virtual environment are added to the module described in Figure 18, which is composed of the input (Figure 18a), the architecture with the GAN (Figure 18b), a deep convolutional network (Figure 18c), and the method to determine the output. Since each approach shares the characteristic vector, this proposal increases the system's performance on embedded devices such as the Jetson nano 2G by Nvidia and the Android device, Moto X4. The updated parameters for embedded networks are shown in Figure 19.  
Figure 16: Model discriminator’s features to validate the training in the GAN. Figure 15: Model features for the GAN’s generator, which estimates the virtual samples from real inputs. Figure 19: Convolutional model’s features that generate the characteristic vector employing the transfer-learning approach. Figure 17: The importance of a GAN for connecting two domains. (**a**) one-to-one dataset. (**b**) One-to-one randomization of output in the dataset. (**c**) Module to change the domain. (**d**) Estimated image. Figure 18: Path generator with the change of the domain. (**a**) Input sample. (**b**) Domain change. (**c**) Feature extraction network. (**d**) Generative path network. Figure 20 summarizes the steps implemented in this development. As the first step, each virtual sample has an associated virtual path generated by the \(A^{*}\) algorithm in the simulator, as shown in Figure 20a. Secondly, three deep-learning solutions are proposed to estimate a path, and the DCNN determines a characteristic vector that is common to them with virtual samples, as illustrated in Figure 20b. The third step describes the connection between both domains through a GAN. This network changes from the real domain to the virtual domain. Thus, the samples in the real domain are reduced to avoid a one-to-one connection, as shown in Figure 20c. Since the GAN requires considerable time, the transfer-learning approach is proposed to reduce this issue. The fourth step is to replace the GAN and characteristic vector called the domain changes with the transfer-learning approach. Therefore, a new model with fewer layers is trained to determinate the characteristic vector, but with fewer operations, as described the Figure 20d. However, it is necessary to consider that the domain change system estimated 50 samples to implement transfer learning. Finally, in the last step, instead of employing the GAN and characteristic vector as separate systems, both are included and placed as estimates of the virtual path with the three deep learning approaches from the real samples, as displayed in Figure 20e. In this way, the cost of the architecture is reduced, and its performance is improved.