  Firefighting is a complex, yet low automated task. To mitigate ergonomic and
safety related risks on the human operators, robots could be deployed in a
collaborative approach. To allow human-robot teams in firefighting, important
basics are missing. Amongst other aspects, the robot must predict the human
motion as occlusion is ever-present. In this work, we propose a novel motion
prediction pipeline for firefighters' squads in indoor search and rescue. The
squad paths are generated with an optimal graph-based planning approach
representing firefighters' tactics. Paths are generated per room which allows
to dynamically adapt the path locally without global re-planning. The motion of
singular agents is simulated using a modification of the headed social force
model. We evaluate the pipeline for feasibility with a novel data set generated
from real footage and show the computational efficiency.
