It’s made ​​with an Arduino  using a printer for receipts to write poem.  The box composes the poems in autonomy, thanks to an algorithm that has been implemented on the Arduino. Push the button and here your dadaist poem. Each poem is different from the previous. It is original, new. Just press a button, and comes out a poem. Simple, immediate, trivial.

Normally, the poem is a valuable asset, is the result of an intimate moment, when the poet transposes on paper the emotions of his soul. It ‘an act inspired, an act of concentration and transport. It’s not immediate. The poem box instead is trivial, it seems almost anti-poem. But not, it’s Dadaist poem.  In one of his writings, the famous poet Tristan Tzara, one of the major poets Dadaists, describes how to produce a Dadaist poem.  The pass following explains their way to proceed:

• Take a newspaper.
• Take some scissors.
• Choose from this paper an article the length you want to make your poem.
• Cut out the article.
• Next carefully cut out each of the words that make up this article and put them all in a bag.
• Shake gently.
• Next take out each cutting one after the other.
• Copy conscientiously in the order in which they left the bag.
• The poem will resemble you.
• And there you are—an infinitely original author of charming sensibility, even though unappreciated by the vulgar herd.

The Dadaist poetry box is based on Arduino Uno. These are the components:

- Arduino Uno

- Sparkfun thermal printer

- A big button

The program is based on pre-defined verses, that Arduino choices with an random alghoritm. The thermal printer is drove by the Arduino, using the SoftwareSerial library. The verses are stored in the program memory of Arduino (almost 7 kb).

The verses are in italian, so if you want the English language, you have to rewrite all the verses!

Some pictures:

This is a little video of the box:

http://www.youtube.com/watch?v=GzF8SV2IIYI

This is the source code: Generatore_di_poesie_V4

]]> http://robottini.altervista.org/dadaist-poetry-box/feed 0 http://robottini.altervista.org/polar-robot-arm-sketching-skyline http://robottini.altervista.org/polar-robot-arm-sketching-skyline#comments Sat, 18 May 2013 10:44:25 +0000 robottini http://robottini.altervista.org/?p=783

I’m often thinking if it is possible to make Art with Arduino. Arduino is a machine, a little computer made with electronic circuits. It hasn’t a soul. Can an Arduino make Art? This is my question.

I’m trying to do something with Arduino that could be Art, or similar to Art. The Arts are painting, poetry, music, sculpture and so on. I’m trying to do something in different Arts. The road is long but I began to travel it. This is my first experiment with painting. Indeed, a easy form of painting, the sketching in Black and White. Arduino drives a robot arm, that with only a pen sketches a city skyline. The skyline is every time different, the buildings are always different. The arm tries to imitate the human sketching, with errors, shaking  lines not parallel and so on. The arm tries to be human, to make a little form of Art. This is the goal, I don’t know if the goal is reached. The judgment is yours.

The robotic arms are among the most fascinating robot that you can build. There are many commercial robotic arms with 4-5-6 axes, while, at amateur level, there are no polar arms.

The arms using polar, instead of Cartesian coordinates (x and y),  the polar coordinates (angle and radius).

So, the point P(x,y) in polar coordinates became P(r,phi). This means a line, which in polar coordinates is described by a very simple equation, in polar coordinates becomes a complex and more difficult, because the radius can increase and decrease while describing the line.

So, which is the advantage using the polar coordinates? The advantage is the robot is very simple to build. I used also an awesome aluminum infrastructure based on Makerbeam (www.makerbeam.eu) , that allows to build the robot similar to the Lego, with the advantage that all is in metal and not in plastic.  Makerbeam is an open-sourced extruded beam construction kit. I got a kit, and this is my first project using it.

The hardware is:

- 1 arduino (2009/uno or Mega)

- a kit of makerbeam for the chassis and 16 ball bearings (www.makerbeam.eu)

- 2 stepper nema 17 400 steps, 0.9° (www.robot-italy.com)

- 2 big easy stepper driver (www.robot-italy.com)

- 2 pignons and 1 gear rack (www.conrad.com)

- 1 little servo motor (www.robot-italy.com)

- 1 pen

This is the robot:

The robot has 2 dof: radius and angle. The forward and inverse kinematics are simple.

The only two shapes that are possible to draw when running one motor at a time is a radial line or a circle segment. The Arduino program implements sort of the Bresenham’s line drawing algorithm to draw segments. Each segment is  divided into small segments (steps). Then, for each step we translate this movement to its corresponding changes in the polar coordinate system, angle and radius. This is finally translated to the number of steps to run each motor.

The robot can have a good precision: the balance is between good speed and good precision. If the robot has a low speed it can be very very precise. When the speed grow the precision goes down. But in this work the precision is not very important. The goal is to imitate a human sketch, and the human when are sketching are not precise.

For the sketch algorithm I’m inspired by this processing sketch: http://www.openprocessing.org/sketch/12095 semplified for Arduino. The robot can work alone, without a computer, all the logic is inside the Arduino.

The main inspiration for the polar arm robot building and for the motor driving software comes from this awesome robot: http://roxen.github.io/polar-plotter/

You can see the robot in action here:

http://www.youtube.com/watch?v=gHZ8i3iptas

The code used for the robot is available here: citta_disegna

The Herkulex are an alternative to more famous motors like the Dynamixel. They have 4 wires and can be drove by a simple microprocessor with a UART (serial) port. My choice is to control them with an Arduino, so I wrote a simple library supporting all the most common Herkulex functionalities.

For the wiring of Herkulex to Arduino, it is important to consider that it is necessary to dedicate a serial port to the communication Arduino-Herkulex.It is not possible (or is not advisable) to use the standard  Arduino port because it is dedicated to communication between PC and Arduino to see the values in the serial monitor. So it is important to use another serial port.

For the Arduino Mega it is easy, because the Mega has 4 serial ports, for the Arduino Uno/2009 that has only one serial port, it is mandatory to use a software serial port. So a library called SoftwareSerial has to be included if we want to use the Arduino Uno/2009.

To do this the last version of SoftwareSerial is not useful, so I included in the library also the right SoftwareSerial version in order to work well with Arduino Uno/2009.

For general knowledge about the Herkulex Dongbu motors, please see the manual: here

Download the library: Herkulex Library for Arduino

These are the commands supported by the library:

I think the Herkulex are a good motors. My tests are not very deep and I don’t stressed the motors, but in general, compared to Dynamixel, I think they are good. The big difference is the possibility to set a large variety of parameters and the possibility to start and stop the movement for all the motors at the same time leaving to motors the definition of acceleration and speed needed to cover the distance assigned in the same time frame. Some time I experimented problems with the Serial port, receiving the data, I tried to limit them in the library, but sometimes these problems are present yet. This depends by the Herkulex motors, not by my library.

These are the details of each command.

begin

beginSerial1

beginSerial2

beginSerial3

End

Initialize

Stat

ACK

set_ID

clearError

torqueON

torqueOFF

moveAll

moveAllAngle

moveSpeedAll

actionAll

moveSpeedOne

moveOne

moveOneAngle

getPosition

getAngle

getSpeed

reboot

setLed

writeRegistryRAM

writeRegistryEEP

Usually a line follower competition is based on pure speed and the path is quite linear and simple, with large curves and wide straights. In this case the hardware part is the most important, because the sw part is quite simple to build. it is important to find the right compromise between the mechanical parts and the engine, to make it running as fast as possible. Unfortunately requires a lot of money, because the engine performance search or the wheels with special tires can be very expensive.The alternative is based on paths that are rather difficult or very twisted, with right angles, intersecting lines, lines breaks, obstacles and so on. In this case the HW is much less important. More important is the algorithm and then the sw. The ability of the robot builder is to build a powerful algorithm that allows the robot to drive fast in every situation.

In this tutorial I would describe the robot that I have realized, that is able to face a tortuous path made ​​of curves at 180 ° rather than tight coils, trying to  have a good speed.
The robot ,according to tradition, was home built. It is also already equipped with an infrared sensor front in case you wish to use it in a path with obstacles.

Questo è Microbo(t) – line follower robot:

It ‘s built with the following elements:
- Chassis 5 mm PVC foam (few cents)
- Arduino Nano (about 40 euros)
- Drive Pololu 50:1 HP (about 30 euros both)
- Pololu motor bracket (both about 6 euros)
- Caster wheel  Pololu 3.8 “plastic (about 3 euros)
- Pololu Wheels 42×19 (both about 10 euros)
- Motor driver SparkFun TB6612FNG (about 9 euros)
- Sharp IR Distance sensor 4-30 cm (about 15 euros)
- Sensor line Pololu QT-8RC (about 12 euros)
- 7.4V 900mAh Battery Li-Po (about 9 euros)
- some screws and wires
As you can see the components expensive are Arduino Nano and the motors. Nothing forbids to use a traditional Arduino with an obvious saving of money. In my configuration it weighs about 160 grams. You can do much better, in fact on the internet there are line follower with Arduino that weigh 100 grams.

Some comments on hw components used. The driving system, formed by Pololu wheels and  motors is almost good. I think that even better is using HP 100:1 Pololu motors which provide even more traction and fluidity in the changes of direction, even if it has a lower number of revolutions/minute. I tested also the 30:1 motors, but they were too nervous. Absolutely bad is the plastic wheel caster form Pololu. Unfortunately I had no other small than this, so I kept it. Not recommend its use. Terrible.

The motor driver SparkFun TB6612FNG is a little undersized. In fact it provides 1.2A at full speed, when the Pololu HP motors  may require up to 1.6A. But I had no problem with these drivers and they still work, so I can only advise them. Unfortunately they have a lot of wires, 7 for driving, 2 for the Vcc voltage (+5 V), one (or two with the ground) for the motor voltage and 4 wires for motors. In total 14 wires that are several. They are simple to program and manage. The full brake function is not good. In fact, if you need to brake, however, takes a little bit to stop, victim of inertia. I think that the engines 100:1 can help on this.

The line sensor Pololu QT-8RC is good. It provides out-of-the-box a digital output and can be connected directly to the Arduino digital ports. After a stage of self-learning during the first few seconds (see the video), it is able to calibrate with the best values ​​taking into account the reflectivity of the surface and the ambient light conditions. It can follow the black lines on white background or white on black background. For this sensor there is an Arduino  library written by Pololu that provides a value between 0 and 8000 depending on the sensor (from left to right) which is above the black line. I modified the library to get a response that goes from -800 to +800 with zero when the line is well below the IR sensor in the middle. This from my point of view makes easier the control. The only real problem I had was in the presence of right angles. In this case the robot has not always stopped in time and managed to turn. Unfortunately I have no videos on this particular problem.

Once built the robot, the biggest difficulty is the control parameters determination, that allow the robot to come back to the line when goes away. In this case I have put in place the usual PID control, although in my version was reduced only in PD, proportional and derivative. The integral control, with the frequent changes of direction, is extremely difficult to detect.
I also wrote  some simple routines for the detection of right angles. In this case the robot tries to stop and turns 90°. This technique has worked quite, but you can sure do better.

For the  IR front sensor I didn’t have any important test for obstacle avoidance. The main problem is due to the need to rotate the robot for the obstacle avoidance operating in open-chain, ie, without feedback.  The robot should pass the obstacle, but that succeeded a few times because the motors battery voltage varies over time, with a variation of path. I did not do further tests, but there are two possibilities:

• a dc-dc converter boost 4-25v ​​for example like in the Pololu 3pi robot that maintains a constant input voltage to the motors and that have a good precision even in the absence of encoder
• the encoders in the wheels with angles measurement

The dc-dc converter costs less than 10 euros, while the cost for the Pololu encoder  is about 25 euros. Maybe in the next version of the Microbo(t) I will decide which solution to adopt. Clearly the solution encoder is more sophisticated but requires more programming effort (and verify that there are still pins available on the Arduino!).

This is the code for Arduino, commented but not very clean. Link

This is the Microbo(t) in action:

http://www.youtube.com/watch?v=2-6_wZ0Lcro

In this example I make the image edge detection. Then the contours detection is obtained and hence the contours are approximated by polynomial curve. In particular, the polynomial is a set of points that can be represented for example with the segments.
The code is organized as a function java but it is simple to adapt for different uses.

// -- opencv linearization variables
int iterazione=0;
int cc=-1;
int [][] E;

// ********************************************************
// OpenCV - Processing with JavaCvPro
// Edge detection - contour detection - polynomial approximation
// ********************************************************
Pimage goFilterCV(PImage draft) {

  E = new int[ow*oh][3]; // matrix for vertex list - weigth * height

  opencv.allocate(ow, oh); //allocate space for image in opencv

  //-- copy draft image to opencv IplImage with javacvpro library
  opencv_core.IplImage opencvImgSrc=opencv.fromPImage(draft);  // copy draft --> IplImage
  opencv_core.CvSize mySize=opencvImgSrc.cvSize();             // take the size of IplImage

  opencv_core.IplImage opencvImgDest= opencv_core.cvCreateImage(mySize, opencv_core.IPL_DEPTH_8U, 3); // build an image IplImage , 3 channels

  //--- bilateral filter effect
  for (int i=0; i<10; i++) {
    opencv_imgproc.bilateralFilter(opencvImgSrc, opencvImgDest, 3, 20.0, 50.0, 0 ); // applique un effet Flou gaussien 
    opencv_core.cvCopy(opencvImgDest, opencvImgSrc);
  }
  filter1 = createImage(ow,oh,RGB);
  filter1=toPImage(opencvImgDest);

  //-- define IplImage
  opencv_core.IplImage iplImgGray;
  opencv_core.IplImage iplImgGray1;
  iplImgGray = opencv_core.cvCreateImage(mySize, opencv_core.IPL_DEPTH_8U, 1);   // build an image IplImage , 1 channels - only gray
  iplImgGray1= opencv_core.cvCreateImage(mySize, opencv_core.IPL_DEPTH_8U, 1);   // build an image IplImage , 1 channels - only gray

  //-- transform colors in gray levels
  opencv_imgproc.cvCvtColor(opencvImgSrc, iplImgGray, opencv_imgproc.CV_RGB2GRAY);

  //-- edge detection - canny algo
  opencv_imgproc.cvCanny(iplImgGray, iplImgGray1, 100.0, 130.0, 3 );
  //opencv_imgproc.cvDilate(iplImgGray1, iplImgGray1, null , 1); // in alternative you can use the dilate function to better find the edges

  //-- contour detection
  opencv_core.CvMemStorage mem = opencv_core.CvMemStorage.create();
  opencv_core.CvSeq contour = new opencv_core.CvSeq(null);
  int total=opencv_imgproc.cvFindContours(iplImgGray1, mem, contour, Loader.sizeof(opencv_core.CvContour.class), opencv_imgproc.CV_RETR_LIST, opencv_imgproc.CV_CHAIN_APPROX_NONE);
  println("total point cvFindContours:"+total);

  //-- polynomial approximation
  while (contour != null && !contour.isNull ()) {
    iterazione++; //number of interactions
    opencv_core.CvSeq poly = null;
    if (contour.elem_size() > 0) {
      poly = opencv_imgproc.cvApproxPoly(contour, Loader.sizeof(opencv_core.CvContour.class), mem, opencv_imgproc.CV_POLY_APPROX_DP, 3, -1);

      //transfer to matrix
      int n = poly.total();
      opencv_core.CvPoint points = new opencv_core.CvPoint(n);
      opencv_core.cvCvtSeqToArray(poly, points.position(0), opencv_core.CV_WHOLE_SEQ);
      for (int i = 0; i < n; i++) {
        cc++;
        opencv_core.CvPoint pt = points.position(i);
        int x = pt.x();
        int y = pt.y();
        E[cc][0]=x;
        E[cc][1]=y;
        E[cc][2]=iterazione;
        // println("pts:"+i+" x:"+x+" y:"+y);
      }
    }
    contour = contour.h_next();
  } //end polynomial approx

 // -- print some values for debugging purposes
 // for (int i = 0; i < 100; i++) //contatore to have all the points
 // println(E[i][0]+" "+E[i][1]+" "+E[i][2]);

  draft=toPImage(iplImgGray1); // return draft - PImage
  return (draft);

}

// ********************************************************
// take an IplImage and give a PImage
// ********************************************************
PImage toPImage (opencv_core.IplImage iplImgIn) { // take an IplImage and give a PImage
  //--- put the IplImage in a bufferedImage
  BufferedImage bufImg=iplImgIn.getBufferedImage(); 

  //---- build a PImage with the same size of IplImage--- 
  PImage imgOut = createImage(iplImgIn.width(), iplImgIn.height(), RGB);

  // put the pixel of IplImage to imgOut.pixels of PImage
  bufImg.getRGB(0, 0, iplImgIn.width(), iplImgIn.height(), imgOut.pixels, 0, iplImgIn.width()); 

  imgOut.updatePixels(); // PImage update

  return(imgOut); // return the PImage
}

Here’s an example of JavaCVPro usage:

// Programme d'exemple de la librairie javacvPro
// par X. HINAULT - octobre 2011
// Tous droits réservés - Licence GPLv3

// Exemple fonction contrast()

import monclubelec.javacvPro.*; // importe la librairie javacvPro

PImage img;

String url="http://www.mon-club-elec.fr/mes_images/online/lena.jpg"; // String contenant l'adresse internet de l'image à utiliser

OpenCV opencv; // déclare un objet OpenCV principal

void setup(){ // fonction d'initialisation exécutée 1 fois au démarrage

        //-- charge image utilisée --- 
        img=loadImage(url,"jpg"); // crée un PImage contenant le fichier à partir adresse web

        //--- initialise OpenCV ---
        opencv = new OpenCV(this); // initialise objet OpenCV à partir du parent This
        opencv.allocate(img.width, img.height); // initialise les buffers OpenCv à la taille de l'image

        opencv.copy(img); // charge le PImage dans le buffer OpenCV

        //--- initialise fenêtre Processing 
        size (opencv.width()*2, opencv.height()); // crée une fenêtre Processing de la 2xtaille du buffer principal OpenCV
        //size (img.width, img.height); // aalternative en se basant sur l'image d'origine

        //--- affiche image de départ --- 
        image(opencv.getBuffer(),0,0); // affiche le buffer principal OpenCV dans la fenêtre Processing

        //--- opérations sur image ---
        opencv.contrast(+50); // applique réglage contraste sur le buffer principal OpenCV

        //--- affiche image finale --- 
        image(opencv.getBuffer(),opencv.width(),0); // affiche le buffer principal OpenCV dans la fenêtre Processing

       noLoop(); // stop programme 
}

void  draw() { // fonction exécutée en boucle

}

The code is very simple. All the opencv instructions are called simply using ‘opencv.’ before the real instruction. You can use the opencv instructions translated in Processing by X. Hinault in the JavaCvPro libraries.

But it is very interesting to mix OpenCv instruction supported by JavaCvPro libraries and native OpenCv instructions. The JavaCvPro allow to use also the native OpenCV instruction. This is a powerful tool!!!

In the example below, only native
OpenCV instructions are used.

// Example of using native OpenCVPro instruction in Processing
// Robottini.altervista.org
// example by X.Hinault modified
// Licence GPLv3

// Bilinear filter example

import com.googlecode.javacv.*;
import com.googlecode.javacv.cpp.*;
import monclubelec.javacvPro.*;
import java.awt.image.BufferedImage;
PImage imgDest, OrigImg;
void setup() {
  size(640, 240);
  String cheminFichier="C:/Users/Nane/Dropbox/Sorgenti Arduino/Jarkman/Foto/gioconda1.jpg";; 
  OrigImg=loadImage(cheminFichier,"jpg"); 
  //---- appel direct des fonctions de la librairie javacv ---- 
  //--- chargement d'un fichier image 
  opencv_core.IplImage opencvImgSrc= opencv_highgui.cvLoadImage(cheminFichier);

  opencv_core.CvSize mySize=opencvImgSrc.cvSize(); // récupère la taille de l'image 

  opencv_core.IplImage opencvImgDest= opencv_core.cvCreateImage(mySize, opencv_core.IPL_DEPTH_8U, 3); // crée une image IplImage , 3 canaux

  //--- application d'effet opencv ---
 for (int i=0; i<20; i++)
    opencv_imgproc.bilateralFilter(opencvImgSrc, opencvImgDest, 3,120.0, 190.00,1 ); // applique un effet Flou gaussien 

//============ récupérer une image openCV dans Processing ===== 
  //--- récupérer l'objet IplImage dans un BufferedImage 
  BufferedImage bufImg=opencvImgDest.getBufferedImage(); // récupère l'image

  //---- créer un PImage --- 
  PImage img = createImage(320,240, PConstants.RGB); 

  // charge les pixels de l'image buffer dans img.pixels
  bufImg.getRGB(0, 0, 320, 240, img.pixels, 0, 320); 
  img.updatePixels();
  image(OrigImg,0,0); 
  image(img,320,0); // affiche l'image
}
void draw() {

}

In this case the OpenCv instructions for Processing are used only in the last part of the code, in order to show the images. The coding of the native OpenCV instructions is more difficult, but very very powerful. It is possible to use almost the whole set of instructions offered by the OpenCv 2.3.1.

The procedures to install opencv 2.3.1 libraries and JavaCvPro are explained in several sites. But I had many difficulties doing an installation in Windows 7, even following the step-by-step guides.

Then I would write a step-by-step procedure that is as simple as possible. The information sources that I used are :

http://code.google.com/p/javacv/wiki/Windows7AndOpenCV

http://www.mon-club-elec.fr/pmwiki_reference_lib_javacvPro/pmwiki.php

This installation is working in windows 7 (32 and 64 bit) with Processing 1.5.  It was tested by me in 4 different pc. These are the steps:

1. Install the runtime components for Microsoft Visual C++ 2010:

• Microsoft Visual C++ 2010 Redistributable Package (x86)
• Microsoft Visual C++ 2010 Redistributable Package (x64)

2. Extract OpenCV-2.3.1-win-superpack.exe inside the root directory C:\. At the end, the opencv are in  C:\OpenCV\

3. Add the ddl path generated by opencv to windows 7 path. This can be made easily with a free program:  RapidEE. Download it from here and launch it.

4. Find the registry key PATH and click on it.

5. Click the right mouse button and choose add value in the menu. Add these values:

• C:\opencv\build\x64\vc10\bin\
• C:\opencv\build\x64\vc10\lib\
• C:\opencv\build\common\tbb\intel64\vc10\

If the win7 32bit version the directory are with x86 instead of x64. For 32bit version add also C:\opencv\build\common\tbb\ia32\vc10\

6. Download the JavaCvPro library (version 0.3) here.

7. Extract and copy all in the Processing library directory: Processing /Mode/java/libraries

8. Download the last version of binary JavaCV  library here (today is  javacv-bin-20120329.zip).

9. Extract and copy all in the Processing library directory: Processing /Mode/java/libraries

10. Create a directory called library inside the javacv-bin directory and put all the file (not the sample directory) inside this new library directory

11. Rename the library javacv-bin extracted with this name: javacv

12. Download the last version of the binary Javacpp library here  ( today is javacpp-0.1-bin.zip)

13. Extract and copy all in the Processing library directory: Processing /Mode/java/libraries

10. Create a directory called library inside the javacpp-bin directory and put all the file (not the sample directory) inside this new library directory

11. Rename the library javacpp-bin with this name: javacpp

If all the steps are succeeded, it is done. Now you can start Processing and test this simple program (take from here):

import com.googlecode.javacv.*;
import com.googlecode.javacv.cpp.*;

import monclubelec.javacvPro.*;

OpenCV opencv; // déclare un objet OpenCV principal

void setup(){ // fonction d'initialisation exécutée 1 fois au démarrage

        opencv = new OpenCV(this); // initialise objet OpenCV à partir du parent This
        opencv.allocate(320,240); // crée le buffer image de la taille voulue

}

void  draw() { // fonction exécutée en boucle

}

It is very important to include also the javacv and javacpp libraries. In the http://www.mon-club-elec.fr documentation this is not required, but in my experience, without these libraries, the program doesn’t work.

To build a robot that draws portraits I need to have a good supply of tools for image processing. The best open source libraries available are the opencv that I would use with Processing.

Unfortunately opencv are available in C + + and python, but not in Java that is the language used in Processing. Luckily some guys  have built some wrappers that allow to use opencv with Processing.

 

Opencv processing library

The most famous library is this: http://ubaa.net/shared/processing/opencv/ which is very well documented. Its installation is simple and works well. But this library  supports only a few commands opencv and doesn’t provide access to thousands of opencv functions.It use QuickTime to perform functions that use the webcam. The commands supported are the most important, however, it is not enough.It allows to write code using opencv in a very simple way, making the primitives opencv a part of Processing language.

For example this is the capture()  function that allocates and initializes resources for reading a video stream from a camera.

import hypermedia.video.*;
OpenCV opencv; //opecv object

void setup() {
size( 320, 240 ); //window size
opencv = new OpenCV( this ); // opencv object
opencv.capture( width, height ); // open video stream
}

void draw() {
opencv.read(); // grab frame from camera
image( opencv.image(), 0, 0 ); } // and display image

The documentation for this library is only in French.I understandFrench well, so I have no problems!

•   Operational Torque: 144 oz-in (10.4 kg.cm)
•   1 / 2 duplex multi-drop serial bus
•   1M bps serial communication
•   Reports position, speed, load, voltage, and temperature
•   Full rotation mode
•   300 ° angular position in 1024 increments
•   Speed ​​and torque control in 1024 increments
•   Built in LED status indicator / alarm
•   Shutdown on max / min voltage, load or temperature
•   Single cable network connection

They are not particularly expensive: about $ 45 U.S., in Europe about 45-50 euros.

These servomotors have difficulty connecting to the Arduino. In fact, the half-duplex communication to 1Mbps requires additional circuitry to make connections to Arduino if there are several servos to be connected. A single servo can be connected directly to the Arduino, in the case of several actuators it is necessary to use a tri-state buffer, which is placed between the Arduino and AX-12A. A simple tri-state buffer is the 74LS241N.

74LS241

The Dynamixel protocol is a serial protocol, so, Arduino side, the buffer 74LS241 must be connected to the serial port and then on pins 0 and 1. This is a problem: if the serial port is used byDynamixel, how to display information through the Arduino IDE Serial monitor?

We need another serial port. The Arduino library NewSoftSerial  can help us, but we also need a USB-serial converter. I chose the converter USB2Serial built from Arduino team.

This is a picture of all the components needed to use the Dynamixel Servos.

The 2 ‘strange’ components: the 74LS241 and the USB2Serial converter:

These are all the components wired:

This is the wiring schema (sorry for the manual drawing):

Dynamixel servos require a software library to work with Arduino. An awesome library, realized by Savage Electronics, can be found here. Instructions can be found here.

This is a short video showing the Dynamixel servos in action. It is possible also to see the values printed in the Serial monitor with the NewSoftSerial throw the Serial port between the pins 8 and 9.

http://www.youtube.com/watch?v=svD-m-NUD3I

This is the code used:

#include <Dynamixel_Serial.h>
#include <NewSoftSerial.h>

NewSoftSerial mySerial(9,8); //9=tx, 8=rx
int k=0;

void setup(){ // Configuration

Ax12.begin(1000000,2); //2=data control
mySerial.begin(9600);  //speed of serial port used by NewSoftSerial
}

void loop(){

// move servo 1 and 2 randomly
Ax12.move(1,random(200,600));
Ax12.move(2,random(200,600));
mySerial.print("Number:");
mySerial.println(k++);

delay(1000);

mySerial.print("Number2:");
mySerial.println(random(100));

}

• The robot geometry which simplifies the equations of motion (kinematics)

• The great idea to anchor the robot upside down so that it has the motors connected to the base. This is very important because while the robot works, the motors remain attached at the base, allowing the robot to have arms very light. Lightness means low friction, low inertia, low power motors, precision, speed, fast acceleration and deceleration.

These are two examples of delta robot:

http://www.youtube.com/watch?v=8G59zTXVHHU

http://www.youtube.com/watch?v=Gv5B63HeF1E

These aren’t laboratory prototypes, but commercial robots and they cost about 50-60.000 euros. I tried to build a simple delta robot with Arduino. My Delta robot works like the commercial Delta robot, ie, in inverse kinematics. Given a point x, y, z in space, in real time Arduino calculates the angles of the servomotors that allow  to reach that point in space.

The Delta robot has two triangular plates. A higher which is fixed and the lower is free. The servo motors are fixed to the top plate. The gripping tool (i.e. an electromagnet, a gripper, a vacuum suction cup), is fixed to the lower plate. The two plates are triangular equilateral, that is, equal sides and equal angles of 60 °.

The Delta  has 3 servo motors and two arms for each servo. One is integral with the servo. The other arm is free to rotate in all directions and it is connected to the lower small triangle. In order to ensure the free turning, the second arm is linked with ball joints. To find the ball joints has not been easy. I found something about the steering of the machine model. Just search on google “joint Uniball” and you can find little cheap joints.

A very good explanation about the the Delta robot geometry and a very effective explanation of inverse and direct kinematics formulas can be found here . You can find also the code that calculates the inverse kinematic.

These are some Delta robot pictures (with an ATtiny85 microcontroller).

The end effector can be used with different tools. I tried a vacuum syringe and  a drawing pen.

These below are some videos with different tools.

The first experiment with a simple trajectory (a circle).

http://www.youtube.com/watch?v=DHdFg0ezqLw

The Delta robot drove by a mouse with Processing. The code is available here.

http://www.youtube.com/watch?v=3DJE0SBv3uU

The Delta robot in  pick and place configuration. The code is available here.

http://www.youtube.com/watch?v=TlmcxDExwd8

The Delta robot drawing. This is a first draft. The code is available here.

http://www.youtube.com/watch?v=JEaGTsSyNfg