An Education and Research Platform for a Fleet of Autonomous Maritime Vehicles

More Information


Duckiepond is an education and research platform of a fleet of autonomous maritime vehicles, and is an offical branch of the Duckietown Foundation. The developments of Duckiepond include 1) the hardware Duckieboat, course materials, . The Duckiepond platform is built upon the Duckietown and AI Driving Olympics platform: Duckieboats rely only on one monocular camera, IMU, and GPS, and perform all ML processing using onboard embedded computers. It supports commonly used middleware (ROS and MOOS-IvP) and containerize software packages in Docker, making it easy to deploy. The powerful learning-based together with classic methods enable important maritime missions: track and trail, navigation, and coordinate among Duckieboats to avoid collisions. The Duckieboat has been operating in a man-made lake, reservoir, river, and seashore environments. The platform puts together the established materials of the Duckietown autonomy education, which has been adopted by universities globally since 2016, and the newly developed materials for maritime missions. All hardware and software materials are openly available, and we wish that the communities across related domains will adopt the platform for education and research.

Hardware Design

Education Materials



Hardware Design

"Duckieboats" are autonomous surface vehicles with the flexibility, capability, machine learning compatibility and objectives of affordability. The Duckieboat system is working on Linux with Ubuntu 16.04 and ROS Kinetic. Based on the underlying system, append PyTorch, MOOS-IvP, NCS SDK to achieve more capability. For the unified and cross-platform needs, we construct the system in Docker container including all software above, dependent libraries and machine learning tools. The structure of Duckieboat is build up with 3D printed materials and layser cut boat and dimensions are 1.5 x 0.75 x 0.6 m. With a weight in air of 20 kg and can carry 10 kg. The system time of autonomy approximately four hours and the power is supply by a 24 voltage Li-battery.

Hardware Design is available on Google Drive links:

 ●   Hardware Parts List

 ●   Duckieboat 3D CAD model

 ●   3D print files

 ●   Layser cut files

Course Materials

Duckiepond Material
Note: This tutorial assumes that the reader is familiar with Robot Operating System(ROS), python and Linux CLI. If not, we suggest you check out Duckietown.

A. Build a duckieboat
B. Software setup
C. Joystick control

Advance material

21. Imitation Learning
21. Single Shot MultiBox Detector
23. Introduction to Reinforcement Learning

Code is available on Github:

 ●   Docker

 ●   Gazebo simulation environment

 ●   Code for real-world Duckieboats


Duckiepond @ NCTU (National Chiao Tung University)


Dr. Hsueh-Cheng 'Nick' Wang, Advisor


Ni-Ching Lin


Yu-Chieh Hsiao


Yi-Wei Huang


Tzu-Kuan Chuang


Pin-Wei Chen


Jui-Te Huang


Chao-Chun Hsu

Duckiepond @ NTU (National Taiwan University)


Dr. Chi-Fang Chen, Advisor


Ching-Tang Hung


2019/04/19 -- Welcome to Prof. Kim from KAIST (Korea Advanced Institute of Science and Technology). Thanks Prof. Kim give us a talk about the reserach experience of autonomous underwater robot.

Contact us

If you are interested, please visit our ARG-NCTU website or contact us with following information.

(EE622)1001 University Road, Hsinchu,
Taiwan 30010, ROC

Pin-Wei Chen