The miniRACECAR

educational program

MiniRacecar is a course specially designed for middle-school students by the Beaver Works Summer Institute (BWSI) of the Massachusetts Institute of Technology (MIT), and is appropriate for students with no or little prior knowledge of programming and robotics.

The course guides students through the programming and assembling of a racecar which will be able to autonomously run through a track with a certain set of obstacles. The course can be fully completed either while assembling the physical car if available, or, in the absence of the physical car, through BWSI’s miniRACECAR software simulation. The course is fully online (asynchronous), is offered at no cost and is in English .

Labs

Their objective is to teach introductory topics in Python, Robotics and computer vision with OpenCV framework.

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Support

Continuous support is provided to teachers by the OpenEdx platform through the Piazza Forum, as well as by the Greek team supportRACECAR Greece.

 Teamwork

Problem

solving

Creativity

Strategy

A program that develops

skills

Python

Robotics

Computer vision

Communication

Labs

Η κάμερα

O αισθητήρας LIDAR

RacecarSim

01

Use the start-update paradigm to create a program which can run on the RACECAR.

Use the drive module to move the RACECAR.

Use the controller module to respond to input from the keyboard in real time.

02

Learn how to use Jupyter Notebooks.

Gain familiarity with OpenCV’s image processing functions.

Use contours to identify the size and locations of important objects.

03

Use depth images to calculate distances between objects and detect the closest one.

Reduce image noise with functions.

Use colored and depth images to detect and calculate the distance of an object.

04

Compare the advantages of using the depth sensing and LIDAR and identify situations where each would be best.

Convert raw LIDAR data into meaningful information about the surrounding environment.

Understand and implement rudimentary path planning.

05

Identify the corner location, orientation, color and ID of AR Markers.

Make decisions based on information provided by AR markers.

Learn about and use Python.