It’s great to hear you enjoyed working with Duckiebots! Tell us a little more about your project and what was your experience like.

My thesis focus was on computer vision based control. I used OpenCV, the well known computer vision library, and the camera mounted on the Duckiebots to extract lane lines from the streets in Duckietown. Based on information extracted from these features, I implemented control laws that enabled the Duckiebot to drive on the streets inside lanes.

To familiarize myself with the platform, I started by taking the Duckietown massive open online course on edX and completed the assignments and homework on my own. One of the modules was about implementing a PID controller for lateral position and another on steering rate control. I enjoyed the Braitenberg vehicles activity too, but my favorite project was on obstacle avoidance, obstacle detection, and computer vision.

Could you tell us more about this project?

For three years now at the University of Nevada, Reno’s Computer Science and Engineering department (CSE) we’ve been conducting a program called Research Experience for Teachers (RET) focused on “Integrating Big Data into Robotics”. It’s a six-week course, through which participants can gain hands-on robotics experience that can be later applied in classrooms, in a fun way. The main idea is trying to provide a research experience to K-12 teachers. That’s why we proposed the idea of using Duckietown to teach K-12 teachers. We asked ourselves. what is the state of the art in terms of data analytics, machine learning? I think Duckietown is a very good education platform for teachers. We make use of the very good materials provided by Duckietown and I’m very satisfied with its implementation.
We purchased a Duckietown set for each participant and let them bring the hardware back to their school. Some teachers started their very own robotics clubs! They basically utilize that as an additional platform for their students.

Nice! And how did that go?

In 2019, I started looking into what we could do with Duckiebots at my at Case Western Reserve University, where I was doing my undergrad studies. After learning about the AI-DO challenges, I was like, well, I’ll give it a try! It’s a challenge. I’m pretty competitive. So it took some trying and it ended up, I would say, good enough for where I was back then.

The other thing I looked into was Duckietown’s large code base for demonstrations because I was mainly working on answering the question: “what can you do with limited computing power for a system?”. I tested out the demos, and the Autolab idea, and tried to work on some improvements.

Back in 2019, there wasn’t a lot of work being done on that, unfortunately, but a good part of what was available had been tested, and the documentation was well-proofread. I then took over as a team lead for University of Toronto’s Autonomous Vehicle team, a role less involved with the project, but I still used Duckietown as a great introduction idea, of course.