## 2 Related Works
In contrast, our study explores the terrestrial autonomous navigation of mobile robots, a distinct area with its complexities, such as interaction with humans and the need to avoid obstacles in real-time. Our contribution is particularly notable in the implementation of data fusion methods to improve robot sensory perception and the introduction of a safety module that allows harmonious coexistence with humans in shared environments. In [15], the use of deep reinforcement learning for fusing multimodal data in the context of action recognition is explored. Their innovative method demonstrates the potential of DRL to effectively combine different types of sensor data, such as visual and inertial, to improve recognition accuracy. While their research focuses on the application of DRL to action recognition, our work also includes the use of multimodal data fusion in autonomous navigation. We employ similar deep learning techniques not for recognition tasks, but to enhance the decision-making process of mobile robots in complex and dynamic environments. In [16], an application of force vision sensor fusion in a learning-based approach for robots that perform the task of pulling doors is presented. Their research highlights the advantages of sensor fusion in improving the interaction of robots with physical objects in their environment. While their application is specific to door-pulling tasks, our research takes a broader approach to autonomous navigation. The fusion strategies we develop are not limited to specific tasks, but are designed to improve the general mobility and safety of robots in human-centric environments. While the authors in [17] focus on sensor fusion applications in high-speed scenarios such as the Indy Autonomous Challenge 2021 and CES 2022, by employing classic methods such as the Extended Kalman Filter (EKF), our study advances the field by integrating cutting-edge techniques in deep learning and reinforcement learning. Sensor fusion in our work is not limited to theories and simulations, but extends to encompass practical applications in robotic systems operating in dynamic environments, a crucial direction for modern autonomous robotics. The authors in [18] discuss the problem of motion-planning efficiency for mobile robots in complex environments such as mazes and S-shaped corridors by employing a Generalized Voronoi Diagram (GVD)-based heuristic path planning method to guide the sampling of Rapidly-exploring Random Trees (RRTs). This method enhances the feature extraction of free space and heuristic path-planning, contributing to real-time motion planning. In contrast, our work focuses on sensor data fusion from GPS, IMU, and RGB-D cameras with deep learning techniques for autonomous navigation of mobile robots, further complemented by a collision avoidance module for moving people detection, increasing safety and efficiency in dynamic environments. In [19], a Deep Reinforcement Learning (DRL) solution for UAV path planning in dynamic environments is proposed, focusing on UAV survival under threats like radar detection and missile attacks. Their approach, based on Dueling Double Deep Q-Networks (DQN), uses situation maps to make navigation decisions. While this work focuses on dynamic aerial environments and external threats, our work aims for autonomous terrestrial navigation, utilizing sensor data fusion and computer vision to safely interact in environments with the presence of people. In [20], a systematic review of AI techniques applied to trajectory planning for UAV swarms is provided. They highlight the growing trend of publications and the evolution of predominant techniques. While their paper offers an overview of AI techniques in a swarm context, our work specifically applies deep learning to sensor data fusion in a single mobile robot, focusing on autonomous and safe interaction in environments that may be shared with humans, showing gains in mission success rates with the implementation of a safety module.