  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.
