## 6 Results
### Application of Sensor Fusion Methods  
After verifying that the DDQN method using only the RGB-D camera did not present sufficient results, the proposals for the fusion of visual and non-visual sensors were applied. The methods implemented were DDQN-Late Fusion and DDQN-Iterative Fusion. Figure 10 shows the moving average of the merger methods with the simple DDQN (visual sensor only). It is possible to visually notice the superiority in terms of the moving average in the Late Fusion method. However, it is also necessary to observe metrics that indicate the quality of navigation.  
The success rate indicates the number of times the AMR completed the mission completely, i.e., passed through the four workstations accurately. Thus, this rate is calculated as the ratio between the amount of success and the number of training episodes. As the metric is evaluated during the learning process, we have the AMR exploration stage where it is not possible to have a 100% success rate due to the randomness of the actions applied in the exploration phase. Normally, this rate evolves during the exploitation phase, and therefore, the values presented are relatively low. The intention is to emphasize that with the proposed method, the success rate increased significantly during learning. Table 2 provides a summary of the navigation metrics during learning.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Proposal** & **Success Rate (\%)** & **Global Reward Average** \\ \hline Late Fusion DDQN & 28.2 & 7.38 \\ \hline Interactive DDQN & 11.6 & 6.43 \\ \hline Pure DDQN & 1.4 & 5.55 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Metrics measured in the learning process.  
Figure 9: Mission trajectory with the Pure DDQN.  
The DDQN-Late Fusion approach exhibited superior performance compared to the Interactive DDQN method. The learning curve for all proposed methods, illustrated in Figure 10, clearly demonstrates this advantage. Moreover, Figure 11 delineates the moving average over the concluding episodes of training, further highlighting the preeminence of the DDQN-Late Fusion technique.  
Upon closer inspection of the learning curves, it is evident that between episodes 150 and 250, the DDQN-Late Fusion method consistently achieved a higher average reward, hovering around 10 points, in stark contrast to the other methods, which averaged around 6 points. While the rewards for both approaches plateaued near the 10-point mark from episodes 250 to 400, a detailed analysis of the span from episodes 400 to 450 reveals a marginal yet noteworthy advantage for the DDQN-Late Fusion.  
It is possible to observe that for the Pure DDQN, the success rate is extremely low when compared to fusion methods. Looking at the average of all rewards obtained (Global Reward Average), the difference is not that big. As previously mentioned, the Pure DDQN starts to fail from station 3.  
Figure 11: Emphasis on the final epochs of training.  
Figure 10: Comparison between the navigation methods with and without fusion.  
It was observed that the integration of non-visual sensors is essential to increase the success rate. Figure 12 illustrates the trajectory of the DDQN-Late Fusion method.  
To complement the findings presented in this paper, a video has been prepared showcasing the navigation of an Autonomous Mobile Robot (AMR) utilizing the DDQN-Late Fusion method. This video serves as supplementary material and offers a visual representation of the method's efficacy in real-world navigation scenarios. The link to this additional content is provided in Supplementary Materials.