Team NCTU: Toward AI-Driving for Autonomous Surface Vehicles -- From Duckietown to RobotX

TL;DR

This paper presents Team NCTU's approach to developing AI-driven autonomous surface vehicles by integrating simulation, middleware, and learning methods, culminating in outdoor testing and competition participation.

Contribution

Introducing a unified AI framework for autonomous surface vehicles that combines simulation, middleware integration, and learning-based approaches for real-world deployment.

Findings

Successful integration of ROS and MOOS middleware solutions.

Development of a platform from simulation to real robots.

Preliminary results from outdoor testing and competition participation.

Abstract

Robotic software and hardware systems of autonomous surface vehicles have been developed in transportation, military, and ocean researches for decades. Previous efforts in RobotX Challenges 2014 and 2016 facilitates the developments for important tasks such as obstacle avoidance and docking. Team NCTU is motivated by the AI Driving Olympics (AI-DO) developed by the Duckietown community, and adopts the principles to RobotX challenge. With the containerization (Docker) and uniformed AI agent (with observations and actions), we could better 1) integrate solutions developed in different middlewares (ROS and MOOS), 2) develop essential functionalities of from simulation (Gazebo) to real robots (either miniaturized or full-sized WAM-V), and 3) compare different approaches either from classic model-based or learning-based. Finally, we setup an outdoor on-surface platform with localization services for evaluation. Some of the preliminary results will be presented for the Team NCTU participations of the RobotX competition in Hawaii in 2018.