Deep Reinforcement Learning Based Autonomous Decision‐Making for Cooperative Uncrewed Aerial Vehicles: A Search and Rescue Real World Application

ABSTRACT This paper presents the first end‐to‐end framework that combines guidance, navigation, and centralized task allocation for multiple UAVs performing autonomous search‐and‐rescue (SAR) in GNSS‐denied indoor environments. A twin delayed deep deterministic policy gradient controller is trained with an artificial potential field (APF) reward that blends attractive and repulsive potentials with continuous control, accelerating convergence and yielding smoother, safer trajectories than distance‐only baselines. Collaborative mission assignment is solved by a deep Graph Attention Network that, at each decision step, reasons over the drone‐task graph to produce near‐optimal allocations with negligible on‐board compute. To arrest the notorious Z‐drift of indoor LiDAR‐SLAM, we fuse depth‐camera altimetry with IMU vertical velocity in a lightweight complementary filter, giving centimeter‐level altitude stability without external beacons. The resulting system was deployed on two 1 m‐class quad‐rotors and flight‐tested in a cluttered, multi‐level disaster mock‐up designed for the NATO‐Sapience Autonomous Cooperative Drone Competition. Compared with prior DRL guidance that remains largely in simulation, our framework demonstrates an ability to navigate complex indoor environments, securing first place in the 2024 event. These results demonstrate that APF‐shaped DRL and GAT‐driven cooperation can translate to reliable real‐world SAR operations.