Robotics-Academy

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Learn Robotics and Computer Vision with JdeRobot

Installation and use[edit]

Robotics Academy uses ROS and Gazebo as underlying infrastructure for the exercises. Computer Vision exercises use OpenCV.

Use it from your web browser with no installation[edit]

Just play with Robotics Academy at its WebIDE, it is free :-)

This spring it is going to be used in a Greek competition 2019 at the University of the Aegean, Greece.

Local installation on Linux machines[edit]

The programming framework is composed of the Gazebo simulator, ROS middleware and the Robotics Academy package. All this software is open source so there are alternative ways to install all of them directly from the source code. Currently we use Gazebo-7.4.0, ROS Kinetic and JdeRobot-Academy (20180606) releases. Follow the installation recipe in the github repository to get the framework up and running, ready to use on your computer.

Local installation on Windows machines[edit]

We prepared docker images which include all the infrastructure software so you can run the framework from the container. Follow the installation recipe in the github repository to get the framework up and running, ready to use on your computer.


Exercises on Computer Vision[edit]

Follow face[edit]

Color filter[edit]

Visual 3D reconstruction from a stereo pair of RGB cameras[edit]

Exercises on autonomous cars[edit]

Qualifying Formula1[edit]

Program a Formula1 car to autonomously complete a lap to the Nürburgring circuit as fast as it can. Compare the evolution of your car with the record along the whole circuit!.

Visual follow-line behavior on a Formula1[edit]

The students program a Formula1 car in a race circuit to follow the red line in the middle of the road.

Local navigation of a Formula1 with VFF[edit]

In a race circuit the students have to program the navigation algorithm of a Formula1 car endowed with a laser (in Gazebo simulator).


Global navigation of a TeleTaxi with GPP[edit]

Global navigation of a TeleTaxi with OMPL[edit]

Autoparking[edit]

Car stop at a joint[edit]

Exercises on mobile robots[edit]

Robot self-localization using particle filter and laser sensor[edit]


Bump and go[edit]

There is a Kobuki robot inside a labyrinth or scenario. The robot will go front until it gets close to an obstacle. The it will go back, turn a random angle and go front again repeating the process. This exercise aims to show the power of automata when building robot behavior.

Using the JdeRobot tool VisualStates the solution works like this. The tool's detailed manual can be found here.

Vacuum-cleaner[edit]

Program a robotic vacuum-cleaner like Roomba to clean your home. It does have a compass but not precise self-localization.

Vacuum-cleaner with visualSLAM[edit]

Program a robotic vacuum-cleaner like Roomba to clean your home. This Roomba model has a precise self-localization algorithm, so its navigation to clean may be better than without localization.

Exercises on drones[edit]

Follow the road[edit]

Program a autonomous drone to follow a road using its onboard cameras. This exercise has been completely refactored to use ROS infrastructure and mavROS.


Drone path planning and navigation in 3D[edit]

Drone position control navigation[edit]

Follow the ground robot[edit]


Drone cat and mouse[edit]

Landing on a moving car[edit]

Escaping from a labyrinth using visual cues[edit]

People rescue after an earthquake[edit]