面向动态多无人机协同计算的分布式轨迹规划与资源分配
Distributed Trajectory Planning and Resource Allocation for Dynamic Multi-UAV Collaborative Computing
AI总结：
本文针对动态参与的多无人机协同计算场景，构建分布式MEC框架，基于斯塔克尔伯格博弈联合优化轨迹与资源，提出分层MADRL算法，仿真显示其能提升无人机效率、降低移动终端开销且适配不同网络规模。
AI中文摘要：
本文研究了一种由多架无人机（UAV）支持的分布式移动边缘计算（MEC）框架，其中协作无人机集合会因自身能量状态和服务负载随时间动态变化。将轨迹规划与资源分配的联合优化问题建模为斯塔克尔伯格博弈，其中无人机被建模为领导者，移动终端（MT）被建模为追随者。无人机旨在通过平衡已执行工作量、能源成本和资源分配收益来最大化自身收益，而移动终端则通过任务卸载和资源请求决策来最小化由计算延迟和资源成本组成的总开销。本文在多智能体深度强化学习（MADRL）框架内开发了一种分层联合优化算法，以分布式方式协调无人机与移动终端。在领导者层级，无人机联合确定自身轨迹、任务迁移率、移动终端-无人机关联以及单位计算资源定价；每架无人机被建模为部分可观察马尔可夫决策过程中的智能体，这些智能体在集中训练-分布式执行范式下通过多智能体近端策略优化（MAPPO）进行联合训练。在追随者层级，移动终端使用两阶段迭代算法确定自身最优任务卸载率和请求的计算资源。仿真结果表明，在无人机动态参与下算法收敛稳定；与无协作基准相比，所提算法通过无人机间任务迁移将无人机效率提升了18.58%，与完全卸载方案相比将移动终端平均开销降低了33.77%；该算法还可通过联合优化无人机操作与资源利用率，在不同网络规模和能力下均优于其他基准算法。
英文摘要：
This paper investigates a multiple uncrewed aerial vehicles (UAVs)-enabled distributed mobile edge computing (MEC) framework, where the set of collaborative UAVs dynamically varies over time due to their energy states and service loads. The joint optimization of trajectory planning and resource allocation is formulated as a Stackelberg game, where UAVs and mobile terminals (MTs) are modeled as leaders and followers, respectively. UAVs aim to maximize their benefits by balancing executed workload, energy cost, and resource allocation revenue, while MTs seek to minimize their total overhead, composed of computing delay and resource costs, through offloading and resource-request decisions. A hierarchical joint optimization algorithm is developed within a multi-agent deep reinforcement learning (MADRL) framework to coordinate UAVs and MTs in a distributed manner. At the leader level, UAVs jointly determine their trajectories, task migration ratios, MT-UAV association, and unit computing resource pricing. Each UAV is modeled as an agent in a partially observable Markov decision process, and the agents are jointly trained via multi-agent proximal policy optimization (MAPPO) under the centralized-training-and-decentralized-execution paradigm. At the follower level, MTs determine their optimal task offloading ratios and requested computing resources using a two-stage iterative algorithm. Simulation results demonstrate stable convergence under dynamic UAV participation. Compared to the no-collaboration benchmark, the proposed algorithm improves UAV efficiency by 18.58% through inter-UAV task migration and reduces average MT overhead by 33.77% over the fully offloading scheme. It also outperforms other benchmarks under varying network scales and capabilities by jointly optimizing UAV operations and resource utilization.
