An Edge-Based Architecture for Offloading Model Predictive Control for UAVs

• Published in: Robotics (Basel) 2022-08, Vol.11 (4), p.80
• Main Authors: Seisa, Achilleas Santi, Satpute, Sumeet Gajanan, Lindqvist, Björn, Nikolakopoulos, George
• Format: Article
• Language: English
• Subjects: 5G mobile communication; Algorithms; Automation; Computer architecture; Control tasks; Data processing; Decision making; Edge computing; Kubernetes; Missions; Mobile computing; model predictive control; Network latency; Payloads; Predictive control; Process controls; Researchers; Robotics; Robotics and Artificial Intelligence; Robotik och artificiell intelligens; Robots; Smart houses; Software packages; UAV; Unmanned aerial vehicles
• ISSN: 2218-6581; 2218-6581
• DOI: 10.3390/robotics11040080

Thanks to the development of 5G networks, edge computing has gained popularity in several areas of technology in which the needs for high computational power and low time delays are essential. These requirements are indispensable in the field of robotics, especially when we are thinking in terms of real-time autonomous missions in mobile robots. Edge computing will provide the necessary resources in terms of computation and storage, while 5G technologies will provide minimal latency. High computational capacity is crucial in autonomous missions, especially for cases in which we are using computationally demanding high-level algorithms. In the case of Unmanned Aerial Vehicles (UAVs), the onboard processors usually have limited computational capabilities; therefore, it is necessary to offload some of these tasks to the cloud or edge, depending on the time criticality of the application. Especially in the case of UAVs, the requirement to have large payloads to cover the computational needs conflicts with other payload requirements, reducing the overall flying time and hindering autonomous operations from a regulatory perspective. In this article, we propose an edge-based architecture for autonomous UAV missions in which we offload the high-level control task of the UAV’s trajectory to the edge in order to take advantage of the available resources and push the Model Predictive Controller (MPC) to its limits. Additionally, we use Kubernetes to orchestrate our application, which runs on the edge and presents multiple experimental results that prove the efficacy of the proposed novel scheme.