Duckiepond: An Open Education and Research Environment for a Fleet of Autonomous Maritime Vehicles Duckiepond is an education and research development environment that includes software systems, educational materials, and of a fleet of autonomous surface vehicles Duckieboat. 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 Duckietown 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 learningbased methods together with classic methods enables important maritime missions: track and trail, navigation, and coordinate among Duckieboats to avoid collisions. Duckieboats have been operating in a manmade lake, reservoir and river environments. All software, hardware, and educational materials are openly available (https:robotxnctu.github.ioduckiepond), with the goal of supporting research and education communities across related domains.

Roboat: An Autonomous Surface Vehicle for Urban Waterways Unmanned surface vehicles (USVs) are typically designed for open area marine applications. In this paper, we present a new autonomy system (Roboat) for urban waterways which requires robust localization, perception, planning, and control. A novel localization system, based on the extended Kalman filter (EKF), is proposed for USVs, which utilizes LiDAR, camera, and IMU to provide a decimeterlevel precision in dynamic GPSattenuated urban waterways. Area and shape filters are proposed to crop water reflections and street obstacles from a pointcloud. Euclidean clustering and multiobject contour tracking are then introduced to detect and track the static and moving objects reliably in urban waters. An efficient path planner is tailored to calculate optimal trajectories to avoid these static and dynamic obstacles. Lastly, a nonlinear model predictive control (NMPC) scheme with full state integration is formulated for the fourcontrolinput robot to accurately track the trajectory from the planner in rough water. Extensive experiments show that the robot is able to autonomously navigate in both the indoor waterway and the cluttered outdoor waterway in the presence of static and dynamic obstacles, implying that Roboat could have a great impact on the future of transportation in many coastal and riverside cities.