DRL-based Task Offloading for Networked UAVs with Random Mobility and Collision Avoidance

Unmanned Aerial Vehicles (UAVs) have gained widespread use across various fields due to their flexibility and multifunctionality. However, their limited onboard computing capacity is often criticized for hindering their ability to execute complex tasks in real-time. To address this challenge, Networked Airborne Computing (NAC) has emerged, which leverages the collective computing power of multiple UAVs to enable efficient handling of large-scale data processing, real-time analytics, and complex mission coordination. Despite its potential, research in this area is still in its infancy. In this paper, we consider a typical NAC scenario where multiple UAVs with collision avoidance capabilities share resources while moving randomly within an area. Without prior knowledge of the system models, we aim to optimize task allocation among UAVs with uncertain mobility. To achieve this, we propose a Deep Reinforcement Learning algorithm based on the Twin Delayed Deep Deterministic Policy Gradient (TD3). Simulation results demonstrate that our approach significantly speeds up task execution compared to existing methods.