Benefiting from the fusion of communication and intelligent technologies, network-enabled robots have become important to support future machine-assisted and unmanned applications. To provide high-quality services for robots in wide areas, hybrid satellite-terrestrial networks are a key technology. Through hybrid networks, computation-intensive and latency-sensitive tasks can be offloaded to mobile edge computing (MEC) servers. However, due to the mobility of mobile robots and unreliable wireless network environments, excessive local computations and frequent service migrations may significantly increase the service delay. To address this issue, this paper aims to minimize the average task completion time for MEC-based offloading initiated by satellite-terrestrial-network-enabled robots. Different from conventional mobility-aware schemes, the proposed scheme makes the offloading decision by jointly considering the mobility control of robots. A joint optimization problem of task offloading and velocity control is formulated. Using Lyapunov optimization, the original optimization is decomposed into a velocity control subproblem and a task offloading subproblem. Then, based on the Markov decision process (MDP), a dual-agent reinforcement learning (RL) algorithm is proposed. The convergence and complexity of the improved RL algorithm are theoretically analyzed, and the simulation results show that the proposed scheme can effectively reduce the offloading delay.

翻译：受益于通信与智能技术的融合，网络赋能机器人已成为支撑未来机器辅助及无人化应用的关键技术。为在广阔区域为机器人提供高质量服务，混合卫星-地面网络是核心技术手段。通过混合网络，可将计算密集型与延迟敏感型任务卸载至移动边缘计算（MEC）服务器。然而，由于移动机器人的机动性与无线网络环境的不确定性，过度本地计算与频繁服务迁移可能显著增加服务延迟。针对该问题，本文旨在最小化卫星-地面网络赋能机器人发起的MEC卸载任务平均完成时间。不同于传统移动感知方案，本方案通过联合考虑机器人移动控制进行卸载决策，并构建了任务卸载与速度控制的联合优化问题。利用Lyapunov优化理论，将原始优化问题分解为速度控制子问题与任务卸载子问题，进而基于马尔可夫决策过程（MDP）提出双智能体强化学习（RL）算法。本文从理论上分析了改进RL算法的收敛性与复杂度，仿真结果表明所提方案能有效降低卸载延迟。