Learning Robots

This wiki site is part of the Learning Robots module.

1. Connecting MATLAB and Arduino

1.1. Arduino support in MATLAB

• MathWorks has a software package to support Arduino directly in MATLAB. You can find the package from its website. Please notice that:

  • You do need to login with your MATLAB account, in order to download the package and get access to other resources such as tutorials.

  • Check out the http://www.mathworks.nl/academia/arduino-software/arduino-matlab.html to find a useful Webinar.

  • In the downloaded zip file, readme.txt has very detailed instructions for installing the firmware to Arduino and the necessary library to MATLAB.

  • if you plan to use servo motors, you need the motor shield for MATLAB to control the motors properly using the given package.

  • To get the online documentation of the arduino class in MATLAB, type in "arduino(" and wait for a dialogue box to pop up:

    

1.2. MATLAB-Processing-Arduino: Using matlabcontrol in Processing

• The matlabcontrol library for Java is available from http://code.google.com/p/matlabcontrol/

• To install the library to be used in Processing:

  1. Download "matlabcontrol jar with source files included" from http://code.google.com/p/matlabcontrol/downloads/list.

  2. Rename the jar file (for example, matlabcontrol-4.0.0.jar) to "matlabcontrol.jar".

  3. Create a directory in your Processing library path: <Sketchbook location>\libraries\matlabcontrol\library, put "matlabcontrol.jar" in this directory.

  4. To verify your installation, start processing, go to menu "Sketch->Import Library", you should be able to able to import "matlabcontrol":

     

• Follow the Walkthrough to understand how to communicate to MATLAB in JAVA.

• Similar approach described in the Walkthrough can be done in Processing. Take the "Hello World" example in Java:

     1 public static void main(String[] args) throws MatlabConnectionException, MatlabInvocationException
     2 {
     3     //Create a proxy, which we will use to control MATLAB
     4     MatlabProxyFactory factory = new MatlabProxyFactory();
     5     MatlabProxy proxy = factory.getProxy();
     6 
     7     //Display 'hello world' just like when using the demo
     8     proxy.eval("disp('hello world')");
     9 
    10     //Disconnect the proxy from MATLAB
    11     proxy.disconnect();
    12 }
  

  The same can be done in Processing by removing the "main" skeleton. However in Processing we need to catch the exceptions:

     1 import matlabcontrol.internal.*;
     2 import matlabcontrol.extensions.*;
     3 import matlabcontrol.*;
     4 
     5 try {
     6   //Create a proxy, which we will use to control MATLAB
     7   MatlabProxyFactory factory = new MatlabProxyFactory();
     8   MatlabProxy proxy = factory.getProxy();
     9 
    10   //Display 'hello world' just like when using the demo
    11   proxy.eval("disp('hello world')");
    12 
    13   //Disconnect the proxy from MATLAB
    14   proxy.disconnect();
    15 } 
    16 catch (Exception e) {
    17   //do nothing, or cry.
    18 }
  

  Other examples in the Walkthrough can be modified in a similar manner for Processing.

• This approach has been used successfully by F.N.A. Al-Kaylani, K.J.F de Greef and N.M. Nelson in their project. See their report: Learning_Robots_SuggestionRobot.pdf

1.3. MATLAB-socket<=>socket-Processing-Arduino: Using TCP/IP client/server sockets

• Both MATLAB and Processign are able to communicate with the others over a TCP/IP connection, acting as either a client or a server.

• Robbert van Vliet, Rik Vegt, Martijn Kors and Michael Geertshuis are able to use this approach in their project. In their report you can find the technical details: Learning_Robots_Socket.pdf

1.4. MATLAB-datafile-Processing-Arduino: Using a data file

• If there is no need of real time communication between MATLAB and processing, you can first let MATLAB write its output to a data file, then let Processing read from it, or vice versa.

• Jelle Dekker, Laura van Geel, Jasper de Kruiff and Vincent Klerkx are very successful in applying this approach to their cleaning robot. See their report: Cleaning_Robot.pdf

2. Other interesting techniques

• NeurophNeuralNetwork

• MaxJavaArduino

• LegoBeyondToys

• Using leJOS with Neural networks