Edge Computing and its Application in Robotics: A Survey

2025-07-01

AI Summary

Edge-computing-enabled robotic systems lack a systematic, comprehensive survey that clarifies their advantages, challenges, and driving factors—particularly concerning ultra-low latency, mobility support, and location awareness. To address this gap, this paper presents the first holistic, systematic survey of edge robotics, analyzing representative works from 2018 to 2023. We rigorously examine core technical dimensions—including architectural design, real-time data processing, networked coordination, and distributed intelligence—identifying common challenges and evolutionary trends. A unified analytical framework is proposed to characterize deployment motivations and performance bottlenecks across key application domains: industrial inspection, autonomous navigation, and human–robot collaboration. Finally, we distill six open research directions: joint communication–computation–control optimization, lightweight edge AI, trustworthy edge coordination, among others. This structured synthesis provides foundational guidance for both theoretical advancement and practical deployment in this rapidly evolving field.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Machine Learning: Learning on the Edge & Model Compression
• Computer Vision: Vision for Robotics & Autonomous Driving

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Cloud, edge and content delivery systems for the Web
• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

The Edge computing paradigm has gained prominence in both academic and industry circles in recent years. By implementing edge computing facilities and services in robotics, it becomes a key enabler in the deployment of artificial intelligence applications to robots. Time-sensitive robotics applications benefit from the reduced latency, mobility, and location awareness provided by the edge computing paradigm, which enables real-time data processing and intelligence at the network's edge. While the advantages of integrating edge computing into robotics are numerous, there has been no recent survey that comprehensively examines these benefits. This paper aims to bridge that gap by highlighting important work in the domain of edge robotics, examining recent advancements, and offering deeper insight into the challenges and motivations behind both current and emerging solutions. In particular, this article provides a comprehensive evaluation of recent developments in edge robotics, with an emphasis on fundamental applications, providing in-depth analysis of the key motivations, challenges, and future directions in this rapidly evolving domain. It also explores the importance of edge computing in real-world robotics scenarios where rapid response times are critical. Finally, the paper outlines various open research challenges in the field of edge robotics.

Problem

• Surveying edge computing's role in robotics applications
• Analyzing challenges and advancements in edge robotics
• Exploring real-time response needs in edge robotics

Innovation

• Edge computing enables real-time robotics AI
• Reduced latency enhances time-sensitive robotics
• Comprehensive survey on edge robotics advancements