Transformer-Based Double Deep Q-Network for AAV Coverage Path Planning and Data Harvesting

Abstract

Autonomous aerial vehicles have become increasingly important for data harvesting tasks in complex urban environments, where efficient area coverage, reliable data collection, and safe navigation are critical. Coverage path planning ensures that all regions of interest are visited with minimal redundancy, while data harvesting focuses on collecting data from distributed IoT sensor nodes under energy and safety constraints. In this work, we propose 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. This strategy combines a compressed global map with a local obstacle-aware map to enable scalable operation and temporal reasoning in complex urban landscapes. The proposed approach is evaluated through extensive simulations on Manhattan32 and Urban50 scenarios and compared against various existing models. Experimental results demonstrate that the proposed method consistently outperforms all baselines by achieving superior coverage efficiency, data collection performance, and landing success rates. Notably, in Urban50 environment, the proposed method improves the coverage ratio by 48.1% and the landing success rate by 14.3% over the baseline models. These results highlight the effectiveness of attention based architectures in enhancing AAV decision-making for coverage path planning and data harvesting tasks while maintaining stringent safety and energy requirements.