Central America is highly vulnerable to natural disasters such as earthquakes and floods, where rapid localization of victims trapped in collapsed structures remains a major bottleneck for Search and Rescue (SAR) teams. At the same time, planetary exploration missions in hostile environments, such as the Moon and Mars, require autonomous multi-agent robotic architectures capable of operating cooperatively without pre-existing infrastructure. This paper presents the design and preliminary validation of a dual-platform system composed of an Unmanned Aerial Vehicle (UAV) and an autonomous Unmanned Ground Vehicle (UGV), conceived as a dual-use technology to enhance victim localization during terrestrial disasters while serving as an analog testbed for cooperative navigation algorithms for planetary exploration. The UAV integrates thermal and RGB cameras processed in real time by a Convolutional Neural Network (CNN) optimized for embedded hardware, detecting human morphological signatures. The UGV operates in confined areas inaccessible to the UAV, using chemical air-quality sensors to detect indirect metabolic indicators such as carbon dioxide (CO2), together with a Simultaneous Localization and Mapping (SLAM) algorithm. Both platforms transmit data via radio frequency to a ground station, where a sensor fusion module correlates the UGV's position with the UAV's GPS coordinates, georeferencing alerts with decimeter-level accuracy. Preliminary tests in high-fidelity simulated environments, with planetary terrain models and structural-collapse scenarios, show the coordinated system achieves three times greater spatial coverage per unit time than manual search patterns, with an 83% detection rate under reduced visibility and alert latency under 25 seconds. The proposed modular architecture enhances civil protection in Central America and establishes an accessible methodological framework for validating robotic systems for future autonomous planetary exploration.