  Swarm robotics is envisioned to automate a large number of dirty, dangerous,
and dull tasks. Robots have limited energy, computation capability, and
communication resources. Therefore, current swarm robotics have a small number
of robots, which can only provide limited spatio-temporal information. In this
paper, we propose to leverage the mobile edge computing to alleviate the
computation burden. We develop an effective solution based on a mobility-aware
deep reinforcement learning model at the edge server side for computing
scheduling and resource. Our results show that the proposed approach can meet
delay requirements and guarantee computation precision by using minimum robot
energy.

Robotics;Machine Learning;Systems and Control;Systems and Control