Collaborative altitude-adaptive reinforcement learning for active search with unmanned aerial vehicle swarms

Active search with unmanned aerial vehicle (UAV) swarms in cluttered and unpredictable environments poses a critical challenge in search and rescue missions, where the rapid localizations of survivors are of paramount importance, as the majority of urban disaster victims are surface casualties. However, the altitude-dependent sensor performance of UAV introduces a crucial trade-off between coverage and accuracy, significantly influencing the coordination and decision-making of UAV swarms. The optimal strategy has to strike a balance between exploring larger areas at higher altitudes and exploiting regions of high target probability at lower altitudes. To address these challenges, collaborative altitude-adaptive reinforcement learning (CARL) was proposed which incorporated an altitude-aware sensor model, a confidence-informed assessment module, and an altitude-adaptive planner based on proximal policy optimization (PPO) algorithms. CARL enabled UAV to dynamically adjust their sensing location and made informed decisions. Furthermore, a tailored reward shaping strategy was introduced, which maximized search efficiency in extensive environments. Comprehensive simulations under diverse conditions demonstrate that CARL surpasses baseline methods, achieves a 12% improvement in full recovery rate, and showcase its potential for enhancing the effectiveness of UAV swarms in active search missions.