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Firefighting is a complex, yet low automated task. To mitigate ergonomic andsafety related risks on the human operators, robots could be deployed in acollaborative approach. To allow human-robot teams in firefighting, importantbasics are missing. Amongst other aspects, the robot must predict the humanmotion as occlusion is ever-present. In this work, we propose a novel motionprediction pipeline for firefighters' squads in indoor search and rescue. Thesquad paths are generated with an optimal graph-based planning approachrepresenting firefighters' tactics. Paths are generated per room which allowsto dynamically adapt the path locally without global re-planning. The motion ofsingular agents is simulated using a modification of the headed social forcemodel. We evaluate the pipeline for feasibility with a novel data set generatedfrom real footage and show the computational efficiency.

Situational Adaptive Motion Prediction for Firefighting Squads in Indoor Search and Rescue