Cooperative Primed Probabilistic Search of People in Industrial Outdoor Environments Using a Heterogeneous Robot Team
Markus Kramer, Jonathan Lichtenfeld, Kevin Daun, Oskar von Stryk · 2025
We propose a cooperative active search framework for localizing people in industrial environments with a team of heterogeneous robots. In contrast to exhaustive search methods, our approach leverages prior information about the initial target distribution and potential behavior to enable primed probabilistic search strategies. To estimate a person's motion trajectory, we combine global path planning with the Social Force Model. We formulate the multi-robot coordination problem as a Mixed Integer Linear Programming (MILP) optimization that accounts for visibility constraints and heterogeneous robot capabilities. Monte Carlo simulation experiments in two real-world industrial scenarios based on simulated and real human trajectory data show that our approach outperforms several baselines. The results demonstrate the effectiveness of combining predictive target modeling for coordinating robot search teams.