AFR: An Efficient Buffering Algorithm for Cloud Robotic Systems

TL;DR

This paper introduces AFR, an efficient buffering algorithm for cloud robotic systems that minimizes data loss impact, improves information quality, and is easy to implement without API changes.

Contribution

The paper presents a formally proven near-optimal buffering algorithm for cloud robots that reduces data loss effects without modifying existing system interfaces.

Findings

Reduces data loss impact by about 20% in remote mapping.

Decreases tracking failure probability from 40-60% to under 10%.

Introduces less than 10 microseconds of overhead.

Abstract

Communication between robots and the server is a major problem for cloud robotic systems. In this paper, we address the problem caused by data loss during such communications, and propose an efficient buffering algorithm, called AFR, to solve the problem. We model the problem into an optimization problem to maximize the received Quantity of Information (QoI). Our AFR algorithm is formally proved to achieve near-optimal QoI, which has a lower bound that is a constant multiple of the unrealizable optimal QoI. We implement our AFR algorithm in ROS without changing the interface or API for the applications. Our experiments on two cloud robot applications show that our AFR algorithm can efficiently and effectively reduce the impact of data loss. For the remote mapping application, the RMSE caused by data loss can be reduced by about 20%. For the remote tracking application, the probability of tracking failure caused by data loss can be reduced from about 40%-60% to under 10%. Meanwhile, our AFR algorithm introduces time overhead of under 10 microseconds.