This research describes the development of an autonomous robotic triage system, carried out by a student through project-based and challenge-based learning methodologies, aimed at solving real-world problems using applied technologies. The system operated in three phases: environment exploration, victim detection through computer vision supported by autonomous navigation, and remote measurement of vital signs. The system incorporated SLAM algorithms for mapping and localization, YOLOv8 pose for human detection and posture estimation, and remote photoplethysmography (rPPG) for contactless vital-sign measurement. This configuration was integrated into a mobile platform (myAGV) equipped with a robotic manipulator (myCobot 280) and tested in scenarios simulating real emergency conditions. All three triage phases defined in this case study were executed continuously and autonomously, enabling navigation in unknown environments, human detection, and accurate positioning in front of victims to measure vital signs without human intervention. Although limitations were identified in low-light environments or in cases of facial obstruction, the modular ROS-based architecture was designed to be adaptable to other mobile platforms, thereby extending its applicability to more demanding scenarios and reinforcing its value as both an educational and technological solution in emergency response contexts.
Angamarca-Avendaño et al. (Wed,) studied this question.