## 2 Related Works  
This section presents some recent works related to the state of the art in the area of sensor (or data) fusion applied to autonomous mobile robots (AMRs). According to [10], Machine Learning techniques can be used to fuse sensory information. It is worth noting that the area of sensory fusion (or multimodal) is not only applied to AMRs. Both sensor fusion and information fusion can be defined as the process of management and treatment of data from various types of sources to improve some specific criteria for decision tasks [4]. The authors in [4] present a comprehensive literature review on the methods of sensor fusion in autonomous vehicles, specifically in the areas of perception, localization, and mapping using machine learning. The authors highlight recent applications in the literature in pedestrian and road detection tasks through the fusion of the LIDAR sensor with images. Other applications are also highlighted, such as positioning with proprioceptive sensors, such as GPS, merged with images. In addition, they present the categorization of the fusion methods existing in the literature. A point that draws attention in the literature review is the absence of reinforcement learning techniques combined with data fusion; the techniques presented are supervised learning. The authors in [11] explain that the area of multimodal fusion applied to robotic systems is still emerging, which reinforces the thesis that in the area of reinforcement learning, this application is still incipient as well. Currently, the medical field, allied with computer and artificial intelligence researchers, employ the use of data fusion to increase the accuracy of several diagnoses that use images (magnetic resonance, X-ray, among others) and tabular data, such as blood tests and a patient's gender and sex. The method described in [7] was called Interactive and uses a channel wise multiplication technique to create attention mechanisms from tabular data in convolutional neural networks, thus unifying the processing of tabular data with images resulting from clinical examinations. In [12], the authors provide a comprehensive review of path-planning strategies for mobile robot navigation. They discuss different algorithms, their applications, and their effectiveness in varied environments, highlighting techniques for overcoming obstacles and achieving goals efficiently. However, our work is distinguished by not only reviewing or comparing existing methods, but also by proposing an innovative approach that integrates sensor data fusion with reinforcement learning techniques, specifically through the implementation of a Double Deep Q-Network (DDQN). Furthermore, while [12] focuses on path-planning strategies in general, our study advances the field by incorporating a real-time collision detection and avoidance module, particularly for people in motion, which is essential for safe operation in dynamic and populated environments. The authors in [13] introduce \"Neural RRT\", a methodology that combines neural networks with the RRT\" algorithm to improve optimal path planning. Their results indicate that machine learning can be effectively integrated into path-planning algorithms to optimize routes in complex environments. In contrast, our study employs sensory data fusion in conjunction with DDQN for autonomous navigation, focusing on detecting and avoiding collisions with moving humans, a practical and critical challenge not directly addressed by \"Neural RRT\". Furthermore, our approach is validated in a dynamic environment that simulates real conditions, where the robot's performance is improved not only in terms of path planning, but also in safe interactions with the shared environment. The work of [14] focuses on path planning for UAVs (Unmanned Aerial Vehicles), using various optimization approaches to address the unique challenges of aerial navigation. The authors offer a detailed analysis of methodologies to optimize the path of UAVs, considering factors such as flight dynamics and airspace restrictions.