Benchmarking MARL for UAV-Assisted Mobile Edge Computing Under Realistic 3D Collision Avoidance Navigation Constraints for Periodic Task Offloading

The rapid growth of Internet of Things (IoT) and Industrial IoT applications has intensified the demand for low-latency and reliable computation support for deadline-constrained periodic real-time tasks. While unmanned aerial vehicles (UAVs) enabling mobile edge computing (MEC) can reduce latency by bringing compute closer to data sources, terrestrial MEC deployments often suffer from limited coverage and poor adaptability to spatially heterogeneous demand. In this paper, we study a multiple-UAV-assisted MEC system serving cluster-based IoT networks, where cluster heads generate deadline-constrained periodic tasks for offloading under strict deadlines. To ensure practical feasibility in dense urban environments, we benchmark UAV mobility using a realistic 3D collision avoidance navigation graph with shortest-path execution, rather than assuming unconstrained continuous UAV motion in free space. On top of this benchmark, we systematically compare three multi-agent reinforcement learning (MARL) paradigms for joint navigation and periodic task offloading: (i) continuous 3D control MARL that outputs motion commands directly; (ii) discrete graph-based MARL that selects collision-free shortest paths; and (iii) asynchronous macro-action MARL. Using a high-fidelity 3D digital twin of San Francisco, we evaluate these paradigms under a unified protocol in terms of offloading success, end-to-end latency, and energy consumption. The results reveal clear performance trade-offs induced by realistic 3D collision avoidance constraints and provide actionable insights for designing UAV-assisted MEC systems supporting periodic real-time task offloading.