M. C. Rodriguez‐Sanchez

This paper describes a methodological study carried out between 2018 and 2022, at Rey Juan Carlos University, focused on the subject monitoring and control systems within a master's program in Industrial Engineering. The study proposes an innovative teaching strategy using problem‐based learning and project‐based learning methodologies. The projects undertaken are based on Internet of Things (IoT) systems aimed at enhancing weather stations , services and facilitating real‐time decision‐making. Inspired by our experience in the development of Industry 4.0 projects, we have designed a methodological strategy for this subject that focuses on providing students with the necessary knowledge and skills in the field of Control and Monitoring Systems and the IoT to develop real monitoring and control systems. The approach emphasizes interdisciplinary problem‐solving, with students working collaboratively in stable teams. Throughout the 16‐week course, tasks of increasing complexity are completed, resulting in the development of a complete system. The practical approach of the course and its relation to real applications motivates students, resulting in better performance. The acquired techniques and skills from the course are broadly applicable across engineering disciplines.

Intervention teams act in hostile scenarios where reducing mission times and accident risks is critical. In these situations, the availability of accurate information about the environment plays a key role in ensuring the well‐being of rescuers and victims. This information required to plan the interventions in indoor emergencies encompasses the location of fires and the presence of dangerous gases. Robotics and remote sensing technologies can help emergency teams to obtain this information in real‐time without exposing themselves. Additionally, the accurate simulation of the environments allows the teams to plan their interventions, creating routes to safely access the affected areas and evacuate the victims. This article presents a robotic solution developed to satisfy the demands of intervention teams. More specifically, it describes an autonomous ground robot that can obtain real‐time location and environmental data from indoor fires, as well as a simulator that reproduces these emergency scenarios and facilitates mission planning. In this way, emergency teams can know the conditions in the scenario before, during, and after the intervention. Thus, risks are minimized by improving their situational awareness and reducing their exposure times during the mission. The system has been developed and validated in collaboration with the end‐users and under realistic harsh environments. During these experiments, the robot was used to detect fire sources and cold smoke and provide environmental information to firefighters. Additionally, the simulator provided alternative routes for accessing and exiting the scene faster and safer by dodging potentially dangerous areas.