Energy-Efficient Data Collection and Task Offloading Optimization in Heterogeneous Multi-Tier AAV Systems via Deep Reinforcement Learning

Autonomous aerial vehicles (AAVs) have gained considerable attention in data collection due to their mobility and flexibility. These capabilities are crucial in time-sensitive missions (e.g., disaster response, military reconnaissance). In such cases, tasks are often subject to tight deadlines and require timely access to information. To address these challenges, this paper investigates collaborative data collection and task offloading in multi-AAV systems, aiming to maximize mission area coverage while minimizing total energy consumption. To overcome the limited computing power of data collection AAVs, we propose a heterogeneous multi-tier AAV system. In this design, an assisted AAV with strong computing capabilities is introduced to handle data offloading and processing. This enhances energy efficiency and enables timely task execution. Consequently, we develop an integrated optimization model to jointly design trajectory planning and task offloading under communication, energy, and deadline constraints. We propose a deep reinforcement learning algorithm called data collection optimized proximal policy optimization (DCOPPO). This approach optimizes both AAV trajectories and offloading decisions. Simulation results demonstrate that DCOPPO significantly outperforms baseline DRL approaches in terms of energy efficiency and task completion performance.