Multi-UAV Trajectory Generation for Fresh Data Collection: A Diffusion-based Reinforcement Learning Approach

This paper investigates the trajectory generation problem for multi-unmanned aerial vehicle (UAV)-enabled uplink data collection. Specifically, we minimize the age-of-information (AoI) and maximize the coverage as well as the amount of collected data by planning the multi- UAV trajectory considering the energy consumption and collisions constraints. Motivated by diffusion models' exceptional generative capabilities, we propose a multi-UAV trajectory generation (MUTG) solution based on soft actor-critic and diffusion to solve the optimization problem. A diffusion model-based predictor is designed to obtain the action policy, where a hierarchical graph-transformer network is developed to extract entities' interactive information as a conditional guide for the diffusion. Numerical results verify the effectiveness and superiority compared with benchmark schemes in terms of average AoI, user coverage and data collection ratio.