Edge Computing Architectures for Intelligent Embedded Robotic Systems
Abstract
The rapid advancement of intelligent embedded robotic systems has significantly transformed industrial automation, healthcare, logistics, agriculture, surveillance, and autonomous transportation. The current vision for robotic platforms is one of fast perception, reasoning and control with strict computational and energy limits of operation. Communication latency, bandwidth bottleneck (the whole data is not always collected in the cloud), privacy and operational reliability loss are well-known issues of traditional cloud-centric architectures deployed for environments where network connectivity may be erratic. Recently, Edge computing have shown a potential to shift computational intelligence closer to robotic devices allowing for low latency processing[10], decentralized intelligence as well as superior autonomy. This paper explores edge computing architectures tailored for intelligent embedded robotic systems through an analysis of the architectural components, computational workflow, communication mechanisms, and performance characteristics. This article presents an overview of the state-of-the-art on edge-oriented robotics, with a focus on how artificial intelligence, Internet of Things (IoT), embedded systems and machine learning models are integrated in current edge environments, while providing context for some existing research challenges. Moreover, the intelligent perception for robot at edge (edge computing), local decision making faculty, collaborative processing on edge and cloud-assisted learning framework will be discussed based on layered architecture. A comparative analysis shows that edge-based robotic systems enhance responsiveness, network efficiency, scalability, and operational reliability as compared to traditional cloud-dependent systems. Emerging trends such as federated learning, digital twins, adaptive edge orchestration and 6G-enabled robotic ecosystems are also discussed. The results demonstrate edge computing as a true enabled technology for the future of autonomous embedded robotic systems to operate smartly, securely and energy-efficiently in dynamic real-world environments.