Duckietown: An open, inexpensive and flexible platform for autonomy education and research
Liam Paull, Jacopo Tani, Heejin Ahn, Javier Alonso-Mora, Luca Carlone, Michal Cap, Yu Fan Chen, Changhyun Choi
Abstract
Duckietown is an open, inexpensive and flexible platform for autonomy education and research. The platform comprises small autonomous vehicles (“Duckiebots”) built from off-the-shelf components, and cities (“Duckietowns”) complete with roads, signage, traffic lights, obstacles, and citizens (duckies) in need of transportation. The Duckietown platform offers a wide range of functionalities at a low cost. Duckiebots sense the world with only one monocular camera and perform all processing onboard with a Raspberry Pi 2, yet are able to: follow lanes while avoiding obstacles, pedestrians (duckies) and other Duckiebots, localize within a global map, navigate a city, and coordinate with other Duckiebots to avoid collisions. Duckietown is a useful tool since educators and researchers can save money and time by not having to develop all of the necessary supporting infrastructure and capabilities. All materials are available as open source, and the hope is that others in the community will adopt the platform for education and research.
BibTeX
@inproceedings{icra2017_duckietownanopen,
title = {Duckietown: An open, inexpensive and flexible platform for autonomy education and research},
author = {Liam Paull and Jacopo Tani and Heejin Ahn and Javier Alonso-Mora and Luca Carlone and Michal Cap and Yu Fan Chen and Changhyun Choi and Jeff Dusek and Yajun Fang and Daniel Hoehener and Shih-Yuan Liu and Michael Novitzky and Igor Franzoni Okuyama and Jason Pazis and Guy Rosman and Valerio Varricchio and Hsueh-Cheng Wang and Dmitry Yershov and Hang Zhao and Michael Benjamin and Christopher Carr and Maria Zuber and Sertac Karaman and Emilio Frazzoli and Domitilla Del Vecchio and Daniela Rus and Jonathan How and John Leonard and Andrea Censi},
booktitle = {ICRA 2017},
year = {2017}
}