## 6 Results
### Practical Implementation  
To evaluate the practical applicability of our proposed method in real-world environments, we conducted experiments using a robotic platform equipped with an NVIDIA Jetson Nano and an RGB camera. This robot is illustrated in Figure 15.  
The main objective of these experiments was to test the performance of the proposed CNN and reward function in real-world conditions, providing a practical validation of our navigation and perception method. To achieve this, the robot was assigned a mission: navigate a track using only the visual part (DDQN + CNN) with the proposed reward function. The robot will not be able to collide with any obstacle outside the track or even leave the track's limitations. This test environment (track) is illustrated in Figure 16. The tests also validate the methodology for transferring weights from neural networks trained from the Coppelia VREP Simulator to a real programmable robot.  
The hardware used for testing consists of an NVIDIA Jetson Nano, a powerful and efficient computing module capable of running multiple machine learning models in parallel and processing sensor data simultaneously. The Jetson Nano was chosen due to its ability to provide sufficient computational power for the real-time execution of convolutional neural networks (CNNs), making it an ideal choice for autonomous robotic applications.  
Figure 16: Mission assigned to the robot: starting point, stopping, and proposed circuit.  
Figure 15: Mobile robot with a Jetson Nano development board.  
One of the key steps in this experiment was the transfer of the CNN weights trained in a simulation environment to the real hardware. This process, known as Sim-to-Real [30], is crucial in validating whether models trained in simulations are capable of performing adequately in real-world settings. The transfer was successfully completed, allowing the proposed CNN to be tested directly on the robotic platform.  
The CNN was configured in a real environment and is presented in Table 1. It is the same one used in a simulation environment. The reward function is given by Equation (4) and is the same one used in our simulations. The visual part of the navigation system as well as the navigation control algorithm are evaluated in the real implementation. Figure 6 shows the visual sensors (displayed in pink) as well as the DDQN-FC module in a real environment. Even being a simple environment, we believe that we can show that our methodology is applicable in a real environment, that is, the mobile robot learning carried out in Coppelia VEP simulations is effectively transferable to a real environment.  
The reward function, on the other hand, was designed to encourage safe and efficient behaviors, penalizing collisions and rewarding progress toward the goal. The practical implementation of these components allowed us to evaluate their performance in real conditions, providing valuable insights for future improvements and adjustments.  
The experiments conducted showed that the proposed CNN and reward function are capable of autonomously guiding the robot in different scenarios, avoiding obstacles, and seeking to reach the defined goal. Figure 17 illustrates one of the trajectories taken by the robot, highlighting the decision points and the actions taken in response to environmental perceptions. A video of the test performed can be found in the Supplementary Materials.  
Despite the promising results, it is important to highlight that the complete implementation of the sensor fusion system, including non-visual sensors, is a crucial step to increase the robustness and reliability of the system in more complex and dynamic environments. This step will be explored in future works, once the necessary sensors are available and integrated into the platform.