## 7 Conclusions  
When evaluating certain missions involving autonomous navigation that require certain precision, it is concluded that it is necessary to merge sensory information to increase the success rate and, consequently, the AMR performance. Sensor fusion is still an incipient area in systems guided via reinforcement learning. With the evolution of Deep Learning, new fusion possibilities are emerging. Thus, the DDQN-Late Fusion methods and an adaptation of the Interactive Fusion method, called DDQN-Interactive Fusion, were proposed in this article. Figure 17: Robot movement frames. It was observed that the DDQN-Late Fusion presented the best performance when compared with the other methods. Despite the authors in [7] stating in their research the superiority of the Iterative Fusion method in classification tasks, in the activity of joining sensors and applying it in reinforcement learning, Late Fusion was superior. This reinforces the thesis that each fusion method must be investigated in its respective applications. Late Fusion had a success rate of \(\approx\)28%, which was higher than Pure DDQN and Iterative DDQN, which had a success rate of \(\approx\)1% and \(\approx\)11%, respectively. It is worth noting that this success rate was calculated during the training process, and for this reason, its values are respectively low since the success rate only increases in the agent's exploitation stage. Another point noted is the effectiveness of the proposed anti-collision safety module, integrated under the control architecture. This module is useful when you have dynamics in the environment, for example, people moving around. The security module has increased the success rate of completing the mission and the advanced rewards through the reinforcement method. The fusion methods proposed in this article are part of a larger control architecture for autonomous navigation that is under development. This architecture also features local control and wireless communication capabilities. Future works intend to implement simultaneous mapping and localization (SLAM) techniques and also integrate them into the system. Developing other methods of sensor fusion is also intended. The scope of this work has laid the groundwork for a comprehensive sensor fusion architecture tailored for autonomous robotic systems. Due to hardware limitations in our current laboratory setup, we have implemented in a real environment and validated only a segment of the proposed architecture in a practical hardware scenario. The full realization of the sensor fusion system on hardware, a critical step towards deploying this technology in real-world applications, is earmarked for future studies. These subsequent investigations will enable us to explore the complete potential of the architecture, addressing the challenges and harnessing the capabilities that were beyond the reach of the present work due to the constraints of the available resources. This planned extension will not only fortify the theoretical findings of this paper, but will also provide a robust platform for benchmarking against real-world complexities and performance criteria. This section presents some supplementary videos that illustrate the tests carried out in simulation and practical environments. (1) Navigation through the DDQN-Late Fusion Algorithm in a Simulation Environment: [[https://www.loom.com/share/684](https://www.loom.com/share/684) afa6a5b0148 afadc9a200]([https://www.loom.com/share/684](https://www.loom.com/share/684) afa6a5b0148 afadc9a200) ab9f3483 (accessed on 6 November 2023); (2) Practical Implementation, Validation of the Proposed CNN Network and Reward Function: [[https://bit.ly/3](https://bit.ly/3) IKIRb5]([https://bit.ly/3](https://bit.ly/3) IKIRb5) (accessed on 6 November 2023). The individual contributions of the authors to the manuscript are as follows: [PERSON] was involved in the conceptualization, methodology, software development, validation, formal analysis, and investigation.