Mobility-Aware Computation Offloading for Swarm Robotics using Deep Reinforcement Learning

TL;DR

This paper introduces a mobility-aware deep reinforcement learning approach for computation offloading in swarm robotics, enhancing energy efficiency and meeting delay and precision requirements through edge computing.

Contribution

It presents a novel deep reinforcement learning model that optimizes computation scheduling and resource allocation in swarm robotics using mobile edge computing.

Findings

Reduces robot energy consumption

Meets delay and precision requirements

Improves computation efficiency in swarm robotics

Abstract

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.