Author: Duckietown Admin

Ubuntu laptop terminal interface with hands operating keyboard, Duckiebot and duckies out of focus in foreground

“Self-Driving Cars with Duckietown” MOOC starting soon

Posted on | by Duckietown Admin

Join the first hardware based MOOC about autonomy on edX!

Are you curious about robotics, self-driving cars, and want an opportunity to build and program your own? Set to start on March 22nd, 2020, “Self-Driving Cars with Duckietown” is a hands-on introduction to vehicle autonomy, and the first ever self-driving cars MOOC with a hardware track!

Designed for university-level students and professionals, this course is brought to you by the Swiss Federal Institute of Technology in Zurich (ETHZ), in collaboration with the University of Montreal, the Duckietown Foundation, and the Toyota Technological Institute at Chicago.

Learning autonomy requires a fundamentally different approach when compared to other computer science and engineering disciplines. Autonomy is inherently multi-disciplinary, and mastering it requires expertise in domains ranging from fundamental mathematics to practical machine-learning skills.

This course will explore the theory and implementation of model- and data-driven approaches for making a model self-driving car drive autonomously in an urban environment, while detecting and avoiding pedestrians (rubber duckies)!

Enroll for free on edX

In this course you will learn, hands-on, introductory elements of:

  • computer vision
  • robot operations 
  • ROS, Docker, Python, Ubuntu
  • autonomous behaviors
  • modelling and control
  • localization
  • planning
  • object detection and avoidance
  • reinforcement learning.
Duckietown Duckiebot model DB21J4 on a lane

The Duckietown robotic ecosystem was created at the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) in 2016 and is now used in over 90 universities worldwide.

“The Duckietown educational platform provides a hands-on, scaled down, accessible version of real world autonomous systems.” said Emilio Frazzoli, Professor of Dynamic Systems and Control, ETH Zurich, “Integrating NVIDIA’s Jetson Nano power in Duckietown enables unprecedented access to state-of-the-art compute solutions for learning autonomy.”

Pedestrian detection: there are many obstacles in Duckietown - some move and some don't. Being able to detect pedestrians (duckies) is important to guarantee safe driving.
Enroll for free on edX

This massive online open course will be have a hands-on learning approach using, for the hardware track, real robots. You will learn how autonomous vehicles make their own decisions, going from theory to implementation, deployment in simulation as well as on the new NVIDIA Jetson Nano powered Duckiebots.

“The new NVIDIA Jetson Nano 2GB is the ultimate starter AI computer for educators and students to teach and learn AI at an incredibly affordable price.” said Deepu Talla, Vice President and General Manager of Edge Computing at NVIDIA. “Duckietown and its edX MOOC are leveraging Jetson to take hands-on experimentation and understanding of AI and autonomous machines to the next level.”

The Duckiebot MOOC Founder’s edition kits are available worldwide, and thanks to OKdo, are now available with free shipping in the United States and in Asia!

Get the Hardware in the USA or Asia
Get the Hardware in the rest of the world

“I’m thrilled that ETH, with UMontreal, the Duckietown Foundation, and the Toyota Technological Institute in Chicago, are collaborating to bring this course in self-driving cars and robotics to the 35 million learners on edX. This emerging technology has the potential to completely change the way we live and travel, and the course provides a unique opportunity to get in on the ground floor of understanding and using the technology powering autonomous vehicles,” said Anant Agarwal, edX CEO and Founder, and MIT Professor.

Enroll now and don’t miss the chance to join in the first vehicle autonomy MOOC with hands-on learning!

Enroll for free on edX

Integrated Benchmarking and Design for Reproducible and Accessible Evaluation of Robotic Agents

Posted on | by Duckietown Admin

Integrated Benchmarking and Design for Reproducible and Accessible Evaluation of Robotic Agents

Why is this important?

As robotics matures and increases in complexity, it is more necessary than ever that robot autonomy research be reproducible.

Compared to other sciences, there are specific challenges to benchmarking autonomy, such as the complexity of the software stacks, the variability of the hardware and the reliance on data-driven techniques, amongst others.

We describe a new concept for reproducible robotics research that integrates development and benchmarking, so that reproducibility is obtained by design from the beginning of the research/development processes.

We first provide the overall conceptual objectives to achieve this goal and then a concrete instance that we have built: the DUCKIENet.

The Duckietown Automated Laboratories (Autolabs)

One of the central components of this setup is the Duckietown Autolab (DTA), a remotely accessible standardized setup that is itself also relatively low-cost and reproducible.

DTAs include an off-the-shelf camera-based localization system. The accessibility of the hardware testing environment through enables experimental benchmarking that can be performed on a network of DTAs in different geographical locations.

The DUCKIENet

When evaluating agents, careful definition of interfaces allows users to choose among local versus remote evaluation using simulation, logs, or remote automated hardware setups. The Decentralized Urban Collaborative Benchmarking Environment Network (DUCKIENet) is an instantiation of this design based on the Duckietown platform that provides an accessible and reproducible framework focused on autonomous vehicle fleets operating in model urban environments. 

The DUCKIENet enables users to develop and test a wide variety of different algorithms using available resources (simulator, logs, cloud evaluations, etc.), and then deploy their algorithms locally in simulation, locally on a robot, in a cloud-based simulation, or on a real robot in a remote lab. In each case, the submitter receives feedback and scores based on well-defined metrics.

Validation

We validate the system by analyzing the repeatability of experiments conducted using the infrastructure and show that there is low variance across different robot hardware and across different remote labs. We built DTAs at the Swiss Federal Institute of Technology in Zurich (ETHZ) and at the Toyota Technological Institute at Chicago (TTIC).

Conclusions

Our contention is that there is a need for stronger efforts towards reproducible research for robotics, and that to achieve this we need to consider the evaluation in equal terms as the algorithms themselves. In this fashion, we can obtain reproducibility by design through the research and development processes. Achieving this on a large-scale will contribute to a more systemic evaluation of robotics research and, in turn, increase the progress of development.

If you found this interesting, you might want to:

Join the AI-DO
Get your Autolab
Join our Slack
Prof. Krinkin

STEM Intensive Learning with Prof. Krinkin

Posted on | by Duckietown Admin | 1 Comment on STEM Intensive Learning with Prof. Krinkin


In the world of engineering education, there are many excellent courses, but often the curriculum has one serious drawback – the lack of good connectivity between different topics. Over in Saint Petersburg, Russia, Kirill Krinkin from SPbETU and JetBrains Research has been using Duckietown to address this problem through an intensive STEM winter course.

STEM Intensive Learning Approach

by Kirill Krinkin

The first part of the school program was a week of classes in the base topic areas which were chosen to complement each other and help students see the connection between seemingly different things – mathematics, electronics and programming.

Of course, the main goal of the program was to give students the opportunity to put their new found knowledge into practice themselves.

Duckietown was the perfect fit for our course because it offered a hands-on learning experience for all of our main topics areas, and once we covered those subject in the first lessons, we challenged the students with much more complex tasks – in the form of projects – in the second half of the course. It made for an exciting and engaging curriculum because students could address a problem, write a program to solve it, and then immediately launch it on a real robot. 

The main advantage of Duckietown compared to many other platforms is that there is a very small learning curve: people who knew nothing about programming and robotics started working on projects after only a few days!

Overview of the course

Part 1 – Main Topic Areas

Subject 1: Linear Algebra

Students spent one day studying vectors and matrices, systems of linear equations, etc. Practical tasks were built in an interactive mode: the proposed tasks were solved individually, and the teacher and other students gave comments and tips.

 

Subject 2: Electricity and Simple Circuits

Students studied the basics of electrodynamics: voltage, current, resistance, Ohm’s law and Kirchhoff’s laws. Practical tasks were partially done in the electric circuits simulator or performed on the board, but more time was devoted to building real circuits, such as logic circuits, oscillatory circuits, etc.

 

Subject 3: Computer Architecture

In a sense, a bridge connecting physics and programming. Students studied the fundamental basis, the significance of which is more theoretical than practical. As a practice, students independently designed arithmetic-logic circuits in the simulator.

 

Subject 4: Programming

Python 2 was chosen as the programming language, as it is used in programming under ROS. After we taught the material and gave examples of solving problems, students were challenged with their own problems to solve, which we then evaluated. 

 

Subject 5: ROS

Here the students started programming robots. Throughout the school day, students sat at computers, running the program code that the teacher talked about. They were able to independently launch the basic units of ROS, and also get acquainted with the Duckietown project. At the end of this day, students were ready to begin the design part of the course – solving practical problems.

Part 2 – Projects

1. Calibration of colors

Duckiebots needs to calibrate the camera when lighting conditions change, so this project focussed on the task of automatic calibration. The problem is that color ranges are very sensitive to light. Participants implemented a utility that would highlight the desired colors on the frame (red, white and yellow) and build ranges for each of the colors in HSV format.

2. Duck Taxi

The idea of this project was that Duckiebot could stop near some object, pick it up and then continue along, following a certain route. Of course, a bright yellow Duckie was the chosen passenger. The participants divided this task into two: detection and movement along the graph.

drive while Duckie is not detected

Duckie identified as a yellow spot with an orange triangle 🙂

Building a route according to the road graph and destination point

3. Building a road map

The goal of this project was to build a road map without providing a priori environmental data for the Duckiebot, relying solely on camera data. Here’s the working scheme of the algorithm developed by the participants:

4. The patrol car

This project was invented by the students themselves. They offered to teach one Duckiebot, the “patrol”, to find, follow, and stop an “intruding” Duckiebot. The students used ArUco markers to identify the Intruder on the road as they are easy to work with and they allow you to determine the orientation and distance of the marker. Next, the team changed the state machine of the Patrol Duckiebot so that when approaching the stop-line the bot would continue through the intersection without stopping. Finally, the team was able to get the Patrol Duckiebot to stop the Intruder bot by connecting via SSH and turning it off. The algorithm of the patrol robot can be represented as the following scheme:

Summary

Students walked away from our STEM intensive learning program with the foundations of autonomous driving, from the theoretical math and physics behind the programming and circuitry to the complex challenges of navigating through a city. We were successful in remaining accessible to beginners in a particular area, but also providing materials for repetition and consolidation to experienced students. Duckietown is an excellent resource for bringing education to life.

After our course ended students were asked about their experience. 100% of them said that the program exceed their expectations. We can certainly say that the Duckietown platform played a pivotal role in our success.