A containerized distributed processing platform for autonomous surface vehicles: preliminary results for marine litter detection

Abstract

Autonomous Surface Vehicles and their management represent one of the significant challenges in coastal and offshore surveying. Although the development of this kind of data acquisition device has skyrocketed in the last few years, line guides and technological solutions still need to come. On the other hand, this kind of robotic vessel's true potential has yet to be explored. This paper presents ArgonautAI, a containerized distributed processing platform for autonomous surface vehicles. The proposed ArgonautAI architecture leverage a cluster of single-board computers with diverse and different characteristics (computing power, CUDA GPUs, FPGAs, GPIOs, PWMs, specialized I/O) orchestrated using Kubernetes and a customized programming interface. Furthermore, the proposed solution introduces two different types of containers: 1) the platform containers hosting the software life support for the platform and 2) the mission containers defined to support the survey mission-specific scopes. The firsts manage the vehicle's instruments (e.g. position, attitude, environment, depth), the data storage, the vessel-to-shore communication, and so on; the latter host mission-specific software components. Finally, as proof of concept of the proposed platform, we present an AI-based marine litter detection application using a hierarchical computer vision approach on heterogenic onboard computing resources.