Duckiepond: An Open Education and Research Environment for a Fleet of Autonomous Maritime Vehicles

Abstract

Duckiepond is an education and research development environment that includes software systems, educational materials, and of a fleet of autonomous surface vehicles Duck-ieboat. Duckieboats are designed to be easily reproducible with parts from a 3D printer and other commercially available parts, with flexible software that leverages several open source packages. The Duckiepond environment is modeled after Duck-ietown and AI Driving Olympics environments: Duckieboats rely only on one monocular camera, IMU, and GPS, and perform all ML processing using onboard embedded computers. Duckiepond coordinates commonly used middlewares (ROS and MOOS) and containerized software packages in Docker, making it easy to deploy. The combination of learning-based methods together with classic methods enables important maritime missions: track and trail, navigation, and coordinate among Duck-ieboats to avoid collisions. Duckieboats have been operating in a man-made lake, reservoir and river environments. All software, hardware, and educational materials are openly available (https://robotx-nctu.github.io/duckiepond), with the goal of supporting research and education communities across related domains.