###### Abstract  
In this work, we propose an approach for the autonomous navigation of mobile robots using fusion the of sensor data by a Double Deep Q-Network with collision avoidance by detecting moving people via computer vision techniques. We evaluate two data fusion methods for the proposed autonomous navigation approach: Interactive and Late Fusion strategy. Both are used to integrate mobile robot sensors through the following sensors: GPS, IMU, and an RGB-D camera. The proposed collision avoidance module is implemented along with the sensor fusion architecture in order to prevent the autonomous mobile robot from colliding with moving people. The simulation results indicate a significant impact on the success of completing the proposed mission by the mobile robot with the fusion of sensors, indicating a performance increase (success rate) of \(\approx\)27% in relation to navigation without sensor fusion. With the addition of moving people in the environment, deploying the people detection and collision avoidance security module has improved about the success rate by 14% when compared to that of the autonomous navigation approach without the security module. D Q-Network Late Fusion and People Detection-Based Collision Avoidance. _Applied Sciences_  
**Citation**: [PERSON], [PERSON]; [PERSON] [PERSON]; [PERSON], [PERSON] Autonomous Robotic Navigation Approach Using Deep Q-Network Late Fusion and People Detection-Based Collision Avoidance. _Applied Sciences_  
**Citation**itation**The ability of autonomous navigation and decision making of these AMRs arises in the function of three major areas of mobile robotics: perception, planning, and action [3]. The sensors present in an AMR are used to sense (perceive) the navigation conditions. The planning area, on the other hand, refers to a list of algorithms embedded in the processors of these robots and are used to decide which path should be followed during the execution of the mission. The actuators are responsible for activating the electromechanical elements that move this robot, such as wheel motors and joints, among others. According to [4], an important consideration in the area of perception is the proper choice of the type of sensor to be used, as well as its ideal configuration. These sensors will be used to mimic the human ability to feel and make decisions. In this way, grouping, or merging, sensors can be a good strategy for optimizing these capabilities, thus expanding the agent's ability to move properly within an environment. Currently, Machine Learning is an important ally in AMR decision making. Machine Learning, a subfield of Artificial Intelligence (AI), is an area of study with great potential for application in learning tasks in autonomous systems. This science can be divided into three categories of problems: (a) Supervised Learning, (b) Unsupervised, and (c) Reinforcement Learning (RL). RL is a powerful machine learning tool for AMR navigation. According to [5], this technique can be applied in sequential systems, where, for example, the agent (a robot) is an environment (for example a production process), possesses certain states (its positions), and the agent performs actions. The actions of this agent generate feedback signals that measure the quality of the action in the environment; this value is called the reward. The objective is to maximize the expected reward value for a given state and action pair. In summary, the agent tends to learn by trial and error and will always seek to maximize the good rewards [6]. One of the advantages of RL is the possibility of navigating a mobile vehicle without pre-planning a route, as the agent learns by iteration and is able to generalize new actions with a possible change in the environment. In this way, the current research work aims to investigate sensory fusion techniques for broad applications involving the navigation of AMRs.