JdeRobot-Academy

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Teaching Robotics and Computer Vision with JdeRobot

Software infrastructure (Ubuntu/Debian)

The programming environment is composed of the (a) Gazebo simulator, (b) JdeRobot middleware and (c) the TeachingRobotics 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-5.2.0, JdeRobot-5.4.0 and TeachingRobotics-0.1.0 releases.

JdeRobot includes the Gazebo plugins, models and configuration files to simulate the robot used in the exercises. Follow the next four steps to have the environment up and running, ready to use.

Install the JdeRobot framework

  • Follow these directions to prepare the package sources
  • Follow these simple directions to install JdeRobot. Just install the jderobot package and that's all (no need for jderobot-deps-dev).

Install the JdeRobot-Academy software

  • Once you have JdeRobot installed in your system, you can download and install the Academy software. To do so, you must:
git clone https://github.com/JdeRobot/Academy.git

Run Exercices

  • Within a shell, go to practice directory in Academy and run it executing:
python2 [practice_file] [config_file]

Software infrastructure (Windows x64)

The programming environment is composed of the (a) Docker with Gazebo simulator, (b) JdeRobot middleware for Python and (c) the TeachingRobotics 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, JdeRobot-5.4.1 and TeachingRobotics-0.1.0 releases.

JdeRobot Docker includes the Gazebo plugins, models and configuration files to simulate the robot used in the exercises. Follow the next four steps to have the environment up and running, ready to use.

Install dependencies

  • Open CMD or powershell and upgrade pip:
python -m pip install --upgrade pip 
  • Install depencencies:
pip3 install numpy zeroc-ice 
pip3 install pyqt5
pip3 install opencv-python
  • Install JdeRobot Python:
pip3 install  http://jderobot.org/store/aitormf/uploads/windows/JdeRobot-0.1.0-py3-none-any.whl 

Download the TeachingRobotics software

With git Shell clone the repository as in Linux and run the exercices with CMD o powershell

git clone https://github.com/jderobot/TeachingRobotics.git

Note: Github repositories are located in Documents\GitHub


Run Exercises

  • Open Kinematics of Docker and push "Docker cli".
  • Run the docker passing the world to use (the first time docker image is downloaded):
docker run -tiP --rm -p 7681:7681 jderobot/jderobot world [world_name]
  • With CMD or PowerShell go to practice directory in TeachingRobotics and run it:
python [practice_file] [config_file]

Exercises on Computer Vision

Color filter

Visual 3D reconstruction from a stereo pair of RGB cameras

Exercises on autonomous cars

Visual follow-line behavior on a Formula1

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

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

Exercises on mobile robots

Bump and go

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.

Exercises on drones

Drone position control navigation

Follow the ground robot

Follow the road

Drone cat and mouse

Landing on a moving car

Escaping from a labyrinth using visual cues

People rescue after an earthquake