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.
@article{arxiv.2306.02705,
title = {Situational Adaptive Motion Prediction for Firefighting Squads in Indoor Search and Rescue},
author = {Nils Mandischer and Frederik Schicks and Burkhard Corves},
journal= {arXiv preprint arXiv:2306.02705},
year = {2023}
}
Comments
published in 5th Workshop on Long-term Human Motion Prediction (LHMP) at International Conference on Robotics and Automation (ICRA) 2023