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How to install Python and PyCharm

This tutorial explains how to install the basic Python environment for the Lecture Maschine Learning. This course requires a Python version >= 3.8 and PyCharm as IDE. In case you are using another operating system you can find some links at the end of this wiki post.

Download and Installation of Python

To program with Python you need a current Python version, which can be downloaded from the following website: Python. Basically every version after 3.8 can be used, quite new versions can still have bugs now and then, furthermore some packages are not yet transferred to the newest version, therefore 3.9 is recommended.

After the download, the program must now be installed. It is important that Python is added to the PATH, otherwise this step must be done manually. This is accomplished by selecting the “Add Python 3.9 to PATH” checkbox. Now Python is installed and the IDE “PyCharm” can be downloaded. 

Installation of PyCharm

To install PyCharm visit the jetbrains website and follow the instructions. We recommend the Professional version, to get the license for it you need to create an account on Jetbrains and log in to the program after the installation. 

Links

CoppeliaSim Tutorial

This tutorial describes the usage of the program CoppeliaSim. In particular, the use of the software in the context of the course “Cyber-Physical-Systems” is discussed.

Download and setup of the program

CoppeliaSim can be downloaded from this website. Furthermore, the Python package “msgpack” must be downloaded via pip. Depending on your operating system, different steps are required after the installation.

Windows 10

For Windows 10, no further installation steps are necessary. However, to use the B0-based remote API, some .dll files must be available in the working directory. These can be found in the installation folder of CoppeliaSim. To shorten the search for the files, you can find all the required files in the GitHub project linked below.

Ubuntu 20.04

Before CoppeliaSim can be started, dependencies for the BlueZero API have to be installed. To accomplish that follow the instructions bellow.

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In contrast to Windows, CoppeliaSim must be started from the terminal on Ubuntu. To do this, right-click on the unpacked folder and select the option “Open in Terminal”. Then enter the following command “./coppeliaSim.sh”. After confirming with the Enter key, CoppeliaSim starts. To use the B0-based remote API, the file “libb0.so” must be available in the working directory. For some simulations, additional files must be added in the same directory, these can also be found in the GitHub project linked below.

CoppeliaSim and B0-based remote API - Python Client

As described above, depending on the operating system, .dll and .so files need to be added to the workspace. Besides the operating system specific files, the Python scripts “b0.py” and “b0RemoteApi.py” must be present in the working directory. These files can be found in the installation folder or in the GitHub project. For the task of the course, the following two applications are most important: actuation, sensing. Sample code for actuation and sensing can also be found in the GitHub project. In the following section, their application is briefly discussed. 

Actuation

Using the API, Two movement modes are implemented in the provided scene “scene_with_pandas.ttt”. These are used with the method “simxCallScriptFunction” in Python because they are programmed as a function in the simulation file. The following modes are available:

  • – pts: In this mode, the angular positions of the joints and the corresponding time are passed to the simulation as a list (all intermediate points are interpolated). This mode is important for control tasks and if the inverse and forward kinematics have been developed by the user.
  • mov: In the “mov”-mode, the positions and speeds of intermediate points are transferred to the simulation. The inverse kinematics of Reflexxes Motion Library type II or IV is used in this mode.

Sensing

At the beginning, the object handles of the observed objects must be determined, this is done with the help of the method “simxGetObjectHandle”. To execute this method you need a so-called topic, more about this down below. Since “simxGetObjectHandle” only needs to be executed once, and only at the beginning or before the simulation, the topic “simxServiceCall” is used.

Afterwards, the joint angles or joint positions can be streamed with the help of the method “simxGetJointPosition” and the position of the end effector with “simxGetObjectPosition”. To achieve this, a callback function is needed for each angle or for the coordinates (one for each xyz triplet). These callback functions are called cyclically and can be used, for example, to store the angles in an array. Finally, it must be noted that the sensor data should always be saved with their time, otherwise no meaningful calculations or diagrams can be made.

As with the actuation, sample codes are available in the GitHub project.

Links

Getting started with LEGO MINDSTORMS Education EV3

What can you do with the LEGO robot sets?

The LEGO Mindstorms Education EV3 sets can be used in different scenarios. They offer a quick and easy introduction into robot control, motion planning and visual navigation from depth images with Python. One can assemble the robots in various ways with different sensors and motors depending on the desired task. 

For more information go to Robot LEGO Robotics EV3 Dev!

Prerequisites

First of all a development set is necessary. At the chair of Cyber-Physical Systems we have five sets available for students. The implementation of the python code and connection to the EV3 can be done with Visual Studio Code and the extension LEGO MINDSTORMS EV3 MicroPython. The EV3 bricks are equipped with a micro-SD card on which the Micropython Image is installed. A more detailed installation guide is provided on GitHub.

Example - Motor control

In the following is an example python code to control a motor with the EV3. At the beginning the motor has to be initialized with the corresponding port (line 8). There are two different ways to control a motor. First, one can set a desired acceleration and target position to run the motor (line 11). Or one can set the desired acceleration and let the motor run until it is stopped by a command (line 17-23). 

Demo

If you want to get the python code or if you are interested in other example codes go to our GitHub repository