The objective of this work is the proposal of a new navigation algorithm and its integration in a platform that is already designed and built, improving the functionality of the robot in order to patrol complex indoor environments. This patrol contains various features related to the navigation and the localization of the platform using a particle filter that allows the robot to move autonomously through the environment with the data obtained from an RGB-D camera and LIDAR. The navigation algorithm is adapted dynamically in real-time using the well-known CNN real-time object detector You Only Look Once (YOLOv3), which we have retrained with our own database. The platform detects standing and fallen people. Additionally, it registers people using a specific face recognition convolutional neural network. All these functionalities are controlled and centralized in a friendly user interface that appears on the robot’s touch screen and a voice service model is also used.