Transported-belief Planning for Multi-agent Search and Rescue under Uncertain Drift

Maritime search for a drifting survivor requires allocating scarce assets while wind and ocean currents continually reshape where the survivor may be. Each route assignment affects both immediate detection and the team’s options for subsequent search. We develop a multi-agent planner that forecasts survivor movement, updates the shared forecast after each non-detection, and coordinates routes by anticipating future search opportunities and teammates’ movements. Our distributionally robust formulation derives detection bounds from the mean and covariance of survivor distributions transported by the forecast flow, capturing how routes share uncertainty through survivor drift—Lagrangian transport—rather than through geographic proximity alone. Across 180 simulated search settings based on OSCAR current data from five ocean regions, the planner achieves 50.1% expected mission success, outperforming common benchmark approaches including classical lawnmower search and reinforcement-learning policies. Ablation studies further show that forward planning and teammate modeling both contribute to performance, with gains sustained across the tested fleet sizes, mission scales, and environment conditions. Our work suggests the importance of coordinating current assignments around predicted survivor movement while preserving valuable opportunities for the team’s next search.