Self-adaptive Multi-Access Edge Architectures: A Robotics Case

arXiv cs.RO / 4/16/2026

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Key Points

The paper argues that compute-intensive AI workloads require adaptive, energy-efficient infrastructure, and proposes intelligent agents to supervise scaling and computation offloading.
It presents a self-adaptive edge computing architecture for a mixed human-robot environment where a neural-network mobility predictor informs proactive robot path planning and human-safety behaviors.
The approach uses a distributed edge offloading system with heterogeneous processing units, orchestrated by Kubernetes, to streamline neural network execution.
A MAPE-K-based adaptation supervisor monitors response times and power consumption to decide when to scale and where to offload computation.
Experimental results indicate improved service quality compared with traditional (non-adaptive) setups, supporting the effectiveness of the architecture for AI-driven robotic systems.

Abstract

The growth of compute-intensive AI tasks highlights the need to mitigate the processing costs and improve performance and energy efficiency. This necessitates the integration of intelligent agents as architectural adaptation supervisors tasked with adaptive scaling of the infrastructure and efficient offloading of computation within the continuum. This paper presents a self-adaptation approach for an efficient computing system of a mixed human-robot environment. The computation task is associated with a Neural Network algorithm that leverages sensory data to predict human mobility behaviors, to enhance mobile robots' proactive path planning, and ensure human safety. To streamline neural network processing, we built a distributed edge offloading system with heterogeneous processing units, orchestrated by Kubernetes. By monitoring response times and power consumption, the MAPE-K-based adaptation supervisor makes informed decisions on scaling and offloading. Results show notable improvements in service quality over traditional setups, demonstrating the effectiveness of the proposed approach for AI-driven systems.