Safe and Energy-Efficient Trajectory Planning for Heterogeneous Multi-UAV Enabled Mobile Edge Computing

Mobile edge computing (MEC) has recently gained significant attention as a promising solution for processing delay sensitive and resource-intensive computational jobs. Existing system schedulers in MEC networks typically assume homogeneous service providers, uniformly distributed user equipment (UE), and identical service requirements, making them unsuitable for practical MEC scenarios where jobs are randomly generated with varying service and completion time requirements. Thus, in this work, we jointly optimize job scheduling and resource allocation in a heterogeneous multi-unmanned aerial vehicle (UAV) enabled MEC network, considering practical factors such as diverse service requirements of jobs, unknown distribution of UEs, and spatial-temporal job arrivals. We aim to reduce the overall job miss rate and the average energy consumption of both UAVs and UEs by jointly planning safe UAV trajectories and onboard resource allocation. To learn uncertain and dynamic UE-side states (e.g., job arrivals and mobility patterns) and ensure the UAV's safety during the flight, we propose a multi-agent safe reinforcement learning algorithm that combines a Shared Soft Actor-Critic architecture for extracting features of heterogeneous UAVs and a two-agent Markov Game of Intervention mechanism for collision avoidance, named SSAC-MGI. In particular, SSAC MGI further incorporates a fine-grained resource allocation scheme to improve onboard resource utilization and reduce job miss rate. Extensive real trace-driven simulations based on Alibaba cluster data validate the effectiveness and superiority of SSAC-MGI, compared with several state-of-the-art algorithms.