## 2 Related Works
The work of [21] focuses on multi-story autonomous navigation for service robots, emphasizing the localization of elevator buttons through computer vision. They deploy a multi-story SLAM system that detects elevator buttons in real-time, enabling the robot to autonomously navigate between floors of a building. This work specializes in indoor navigation challenges and employs computer vision algorithms to overcome common issues in elevators. In comparison, our work is concerned with mobile robots in varied environments, employing sensor data fusion and deep learning to ensure safe and efficient navigation, especially in the presence of moving humans. In [22], autonomous UAV path planning for target coverage problems using artificial intelligence methods, including genetic algorithms, ant colony optimization, Voronoi diagrams, and clustering methods, are discussed. They focus on enhancing the genetic algorithm's initial population to expedite convergence and prevent UAV crashes by integrating Voronoi vertices and cluster centers as navigational waypoints. In contrast, our work concentrates on the autonomous navigation of ground-based mobile robots through sensor data fusion using a Double Deep Q-Network and a collision avoidance module that detects moving people using computer vision techniques. While they optimize UAV path efficiency and terrain collision avoidance, our study enhances sensor fusion applications for mobile robots, with a demonstrated improvement in mission success rate when integrating safety modules that consider dynamic human presence. The work of [8] offers a thorough review of multi-modal perception techniques for autonomous driving. Our research applies fusion methodologies directly within the framework of reinforcement learning for autonomous robot navigation. Our contribution specifically addresses the integration of sensory inputs (GPS, IMU, and RGB-D camera) to facilitate the robot's decision-making process in real-time scenarios with pedestrian traffic. We provide empirical evidence of our method's success, showcasing approximately a 27% increase in mission completion rates through sensor fusion and a further 14% improvement with the addition of our collision avoidance module. This not only illustrates the practical implementation of sensor fusion, but also highlights its importance in the context of interactive environments where safety and adaptability are paramount. The distinction of our approach lies in the use of deep neural networks and reinforcement learning algorithms that are essential for the intelligent and adaptive navigation of robots, paving the way for significant advances in interaction and safety in environments shared with humans. While comprehensive implementation remains a goal for the future, current phases of our research already demonstrate the feasibility and potential of our methods. Furthermore, we even slightly explore in this paper Sim-to-Real knowledge transfer as a proof of concept for the practical applicability of our automated learning strategies, confirming the adaptability and efficiency of the proposed algorithms. This aspect highlights the relevance of our contribution to autonomous navigation, where the challenges are not only theoretical, but also practical and operational. The next section describes some technologies that allow the implementation of efficient navigation systems, such as reinforcement learning and sensor fusion.