Team NCTU: Toward AI-Driving for Autonomous Surface Vehicles -- From Duckietown to RobotX
This work addresses the problem of system integration and evaluation for autonomous surface vehicles, particularly for teams in competitions like RobotX, but it is incremental as it applies existing principles to a new domain.
The paper tackles the integration of autonomous surface vehicle systems by adopting AI-DO principles from Duckietown to the RobotX challenge, using containerization and uniformed AI agents to facilitate middleware integration, simulation-to-real development, and approach comparison, with preliminary results from the 2018 RobotX competition.
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.