DAOMAN: Decentralized Adaptive Computation Offloading and Multi-Agent Navigation for Satellite-Ground-Network Robots

Robots usually appear in the form of robot swarms when they execute complex tasks. Given cost constraints that lead to insufficient computational capabilities in robots, they can offload computation-intensive tasks to mobile edge computing (MEC) servers via satellite-ground network, substantially improving their efficiency in real-world task execution. However, robots' mobility causes their positions relative to MEC servers to change continuously, resulting in dynamically evolving computing offloading environments. To address this issue, this paper aims to ensure the efficiency of multiple robots jointly completing a complex real-world task and at the same time minimize the average completion time of the robot's computing task. We consider a scenario in which multiple robots move to multiple target locations, where the destination of each robot is not assigned in advance. The robots need to move to one of the target locations individually, and at the same time, their own computing tasks can be offloaded to base stations or satellites around them. We conceptualize this problem as a multi-objective optimization framework, which is decoupled into two components: movement control and computation offloading, and propose the DAOMAN algorithm. Simulation results show that the proposed method minimizes the time for robots to reach their respective targets while also reducing the completion time of their computation tasks.