In the realm of digital situational awareness during disaster situations, accurate digital representations, like 3D models, play an indispensable role. To ensure the safety of rescue teams, robotic platforms are often deployed to generate these models. In this paper, we introduce an innovative approach that synergizes the capabilities of compact Unmaned Arial Vehicles (UAVs), smaller than 30 cm, equipped with 360 degree cameras and the advances of Neural Radiance Fields (NeRFs). A NeRF, a specialized neural network, can deduce a 3D representation of any scene using 2D images and then synthesize it from various angles upon request. This method is especially tailored for urban environments which have experienced significant destruction, where the structural integrity of buildings is compromised to the point of barring entry-commonly observed post-earthquakes and after severe fires. We have tested our approach through recent post-fire scenario, underlining the efficacy of NeRFs even in challenging outdoor environments characterized by water, snow, varying light conditions, and reflective surfaces.

Earthquakes, fire, and floods often cause structural collapses of buildings. The inspection of damaged buildings poses a high risk for emergency forces or is even impossible, though. We present three recent selected missions of the Robotics Task Force of the German Rescue Robotics Center, where both ground and aerial robots were used to explore destroyed buildings. We describe and reflect the missions as well as the lessons learned that have resulted from them. In order to make robots from research laboratories fit for real operations, realistic test environments were set up for outdoor and indoor use and tested in regular exercises by researchers and emergency forces. Based on this experience, the robots and their control software were significantly improved. Furthermore, top teams of researchers and first responders were formed, each with realistic assessments of the operational and practical suitability of robotic systems.

Climate change is leading to more and more extreme weather events such as heavy rainfall and flooding. This technical report deals with the question of how rescue commanders can be better and faster provided with current information during flood disasters using Unmanned Aerial Vehicles (UAVs), i.e. during the flood in July 2021 in Central Europe, more specifically in Erftstadt / Blessem. The UAVs were used for live observation and regular inspections of the flood edge on the one hand, and on the other hand for the systematic data acquisition in order to calculate 3D models using Structure from Motion and MultiView Stereo. The 3D models embedded in a GIS application serve as a planning basis for the systematic exploration and decision support for the deployment of additional smaller UAVs but also rescue forces. The systematic data acquisition of the UAVs by means of autonomous meander flights provides high-resolution images which are computed to a georeferenced 3D model of the surrounding area within 15 minutes in a specially equipped robotic command vehicle (RobLW). From the comparison of high-resolution elevation profiles extracted from the 3D model on successive days, changes in the water level become visible. This information enables the emergency management to plan further inspections of the buildings and to search for missing persons on site.

Current research is aligned with the need of rescue workers but robustness and ease of use remain significant barriers to adoption, NCCR Robotics researchers find after reviewing the field and consulting with field operators. Robots for search and rescue are developing at an impressive pace, but they must become more robust and easier to use in order to be widely adopted, and researchers in the field must devote more effort to these aspects in the future. This is one of the main findings by a group of NCCR Robotics researchers who focus on search-and-rescue applications. After reviewing the recent developments in technology and interviewing rescue workers, they have found that the work by the robotics research community is well aligned with the needs of those who work in the field. Consequently, although current adoption of state-of-the-art robotics in disaster response is still limited, it is expected to grow quickly in the future.

INTF-1 technical search team as it arrived on site and conducted a reconnaissance of the collapsed building. The scientists also got to observe the process by which the search team manager decided whether to use traditional tools, such as acoustic sensors or search cameras, or a robot (figure 2). The robots were deployed the National Science Foundation's Each scientist went into with rescue workers as they went the rubble at least two times and witnessed through a complete deploy-searchcleanup the deployment of each brand Search and Rescue (CRASAR) at cycle or "evolution." Embedding under such realistic conditions permitted the participants to gain an ethnographic understanding of rescue robotics, direct access to one type of collapse site, and an introduction to standard operating procedures such as decontaminating the robots that might impact the design of better robots and software. The scientists brought sleeping bags and slept during the single four-hour rest cycle allotted to the rescue workers.