A Kubernetes-Based Edge Architecture for Controlling the Trajectory of a Resource-Constrained Aerial Robot by Enabling Model Predictive Control

TL;DR

This paper presents a novel Kubernetes-based edge architecture designed to enable real-time model predictive control of resource-constrained UAVs, improving trajectory control by leveraging edge computing capabilities.

Contribution

It introduces a new edge architecture utilizing Kubernetes for UAV trajectory control with MPC, addressing latency issues in resource-constrained environments.

Findings

Enhanced real-time control of UAVs using edge computing

Reduced latency in trajectory planning and control

Successful implementation of Kubernetes for UAV resource management

Abstract

In recent years, cloud and edge architectures have gained tremendous focus for offloading computationally heavy applications. From machine learning and Internet of Thing (IOT) to industrial procedures and robotics, cloud computing have been used extensively for data processing and storage purposes, thanks to its "infinite" resources. On the other hand, cloud computing is characterized by long time delays due to the long distance between the cloud servers and the machine requesting the resources. In contrast, edge computing provides almost real-time services since edge servers are located significantly closer to the source of data. This capability sets edge computing as an ideal option for real-time applications, like high level control, for resource-constrained platforms. In order to utilize the edge resources, several technologies, with basic ones as containers and orchestrators like Kubernetes, have been developed to provide an environment with many features, based on each application's requirements. In this context, this works presents the implementation and evaluation of a novel edge architecture based on Kubernetes orchestration for controlling the trajectory of a resource-constrained Unmanned Aerial Vehicle (UAV) by enabling Model Predictive Control (MPC).