Integration of the Mobile Robot and Internet of Things to Monitor Older People

This study proposes to develop, build and implement the IoT of the mobile network robot integrated with features of mapping and location in an internal environment, to trace the best route and obtain a fast and efficient change to detect changes in the environment as an identifier of falls in the elderly. It is observed that it is applied research to aggregate several available algorithms. The robot was provided with internal mapping and location capabilities to map the best route and achieve fast and active movements in a retirement home for the elderly. The mobile robot was also set up to monitor and assist in the transport of medicines and notify the caregiver of any incident with the elderly within its environment. A mobile app controls system and robot development. The main phases are highlighted: definition and acquisition of the model and the components used (mechanical structure, microcontroller, sensors and actuators); application development of user-system interaction; development and construction of a robot, auxiliary modules (environment) and central module; and integration experiments. Monitoring and mapping of the environment are performed using Wall-Following, Simultaneous Location and Mapping (SLAM) and Sensor Fusion techniques. The precise movements of the robot are assured through a combination of navigation and control techniques. Moreover, the robot received internal mapping and location, resources to map the best route and obtain quick and active movements in Institutions of Long Stay for the Elderly (L.T.C.F.). Besides, the performance of the following algorithms was analyzed and compared: Breadth-First Search (B.F.S.), Depth First Search (D.F.S.), and Wall-Following. The B.F.S. algorithm obtained the best results for the minimum path.