Transformer-Based Double Deep Q-Network for AAV Coverage Path Planning and Data Harvesting
A Transformer-enhanced Double Deep Q-Network (TDDQN) framework that effectively integrates a Standard Transformer Encoder with a hierarchical global-local map representation for large-scale urban AAV navigation to enable scalable operation and temporal reasoning in complex urban landscapes is proposed.