A Measurement Study on Edge Computing for Autonomous UAVs

Abstract

The ability to execute complex signal processing and machine learning tasks in real-time is the core of autonomy. In airborne devices such as Unmanned Aerial Vehicles (UAV), the hardware limitations imposed by the weight constraint make the continuous execution of these algorithms challenging. Edge and fog computing can mitigate such limitations and boost the system and mission-level performance of the UAVs. However, due to the UAVs motion characteristics and complex dynamics of urban environments, the performance of pipelines using interconnected, rather than onboard, resources can quickly degrade. Motivated by the development of Hydra, an architecture for the establishment of flexible sensing-analysis-control pipelines over autonomous airborne systems, this paper reports a preliminary measurement study on the performance of computing task offloading on available network technologies in this class of applications and systems.