Fog Robotics for Efficient, Fluent and Robust Human-Robot Interaction

TL;DR

This paper proposes Fog Robotics as an improved architecture over Cloud Robotics to enhance responsiveness, reduce latency, and address security issues in human-robot interaction by processing data closer to robots.

Contribution

It introduces Fog Robotics architectures that leverage Fog Computing to improve latency, security, and scalability in human-robot interaction systems.

Findings

Fog Robotics reduces latency compared to Cloud Robotics.

Enhanced security features address privacy and ransomware concerns.

Experimental results demonstrate improved responsiveness in robotic interactions.

Abstract

Active communication between robots and humans is essential for effective human-robot interaction. To accomplish this objective, Cloud Robotics (CR) was introduced to make robots enhance their capabilities. It enables robots to perform extensive computations in the cloud by sharing their outcomes. Outcomes include maps, images, processing power, data, activities, and other robot resources. But due to the colossal growth of data and traffic, CR suffers from serious latency issues. Therefore, it is unlikely to scale a large number of robots particularly in human-robot interaction scenarios, where responsiveness is paramount. Furthermore, other issues related to security such as privacy breaches and ransomware attacks can increase. To address these problems, in this paper, we have envisioned the next generation of social robotic architectures based on Fog Robotics (FR) that inherits the strengths of Fog Computing to augment the future social robotic systems. These new architectures can escalate the dexterity of robots by shoving the data closer to the robot. Additionally, they can ensure that human-robot interaction is more responsive by resolving the problems of CR. Moreover, experimental results are further discussed by considering a scenario of FR and latency as a primary factor comparing to CR models.