Demo Abstract: CARL: Collaborative Altitude-Adaptive Reinforcement Learning for Active Search with UAV Swarms

Sensing noise and complex decision-making pose critical challenges to active search for lost persons amid disasters, impeding efficient rescue efforts. We introduce CARL, a collaborative altitude-adaptive reinforcement learning framework for UAV swarms. CARL integrates confidence-informed assessment with Sparse Bayesian Learning to diminish the noise impact on sensor performance, and an altitude-adaptive planner for collaborative active search strategy. Simulation experiments with up to 50 targets and 10 UAVs demonstrate CARL’s superior performance compared to baseline methods in lost person active search scenarios.