Difference between revisions of "Roomba project"

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(Wall following algorithm)
(Wall following algorithm)
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These two lines are needed to import from Pyro library the finite state machine and brain structure. They let us to use the algorithm as a brain for the Roomba, using the FSM concepts.
 
These two lines are needed to import from Pyro library the finite state machine and brain structure. They let us to use the algorithm as a brain for the Roomba, using the FSM concepts.
 +
  
 
First state: findWall.
 
First state: findWall.
Line 90: Line 91:
 
   
 
   
 
     def onActivate(self):  
 
     def onActivate(self):  
         print "Inizio"
+
         print "Start"
 
   
 
   
 
     def update(self):
 
     def update(self):
Line 101: Line 102:
 
           self.goto('turnSx')
 
           self.goto('turnSx')
  
 
+
When the Roomba is in this state (e.g when we start the brain), he will just go straightforward until he finds a wall in front of him. Here the function onActivate(self) just prints out the state in which the robot has just entered (and so it is for the other states as well).
 +
The function update(self) is called periodically, while Roomba is in the current state: in this case
 
   
 
   
 
  class turnSx (State):
 
  class turnSx (State):
 
    
 
    
 
     def onActivate(self):  
 
     def onActivate(self):  
         print "Stato turnSx"
+
         print "State turnSx"
 
 
 
 
 
     def update(self):
 
     def update(self):
Line 121: Line 123:
 
   
 
   
 
     def onActivate(self):
 
     def onActivate(self):
         print "Stato correctDx"
+
         print "State correctDx"
 
   
 
   
 
     def update(self):
 
     def update(self):
Line 140: Line 142:
 
    
 
    
 
     def onActivate(self):
 
     def onActivate(self):
         print "Stato correctSx"
+
         print "State correctSx"
 
          
 
          
 
     def update(self):
 
     def update(self):

Revision as of 17:30, 25 August 2010

Roomba Analysis and Modification
Image of the project Roomba project
Short Description: Roomba's control system study and modification
Coordinator: GiuseppinaGini (gini@elet.polimi.it)
Tutor: ThomasFerrari (tferrari@elet.polimi.it)
Collaborator:
Students: AndreaScalise (andrea.scalise@aol.it), NiccoloTenti (nicotenti@libero.it)
Research Area: Robotics
Research Topic: Robot development
Start: 2010/05/10
End: 2010/09/25
Status: Active
Level: Ms
Type: Course

This project is about studying the vacuum cleaner Roomba's brain, in order to modify and add new behaviours to it: in particular a wall-following algorithm will be implemented and tested. The interfacing with the robot will be provided by Pyro, a library, environment, graphical user interface to explore AI and robotics using the Python language.

Interfacing with Pyro

Pyro is a powerful software written in Python, used to provide a high level interface to many types of robots, regardless of their hardware structure. Moreover, Pyro provides an efficient simulation enviroment of the most common robots, so that you can implement your own algorithms for these robots even if they are not at your own disposal. Unfortunately though, a simulator for Roomba has not been implemented yet, so you need the physical robot in order to work on it. Here are the main steps to configure properly the Pyro environment and to interface it with a Roomba. For the project a Roomba 535 will be used (the one shown in the photo).

Pyro: download and installation

First of all, you need to download the latest version of Pyro. You can choose between different versions and operative systems from this website: http://pyrorobotics.org/download/. Since the project will be developed under Linux, we will refer to Linux Ubuntu OS, using the Pyro version 5.0.0. Then, you need to install it, open a terminal, move to the pyrobot folder, write the following commands and follow the instructions:

./configure.py
make
makefile

If you need Pyro only for this project, we suggest to avoid to install all the optional functionalities needed for the camera support (since Roomba doesn't have any).

If you have python version 2.6 or higher, you need to modify the following file in the pyrobot folder:

/pyrobot/bin/pyrobot

changing the words "site-packages" with "dist-packages" wherever it appears. Attention: this modification must not be done if you have earlier python versions. From now on, we will refer to python version 2.6.

Then you should move the pyrobot folder you have downloaded (after the installation) to the folder

/usr/lib/python2.6/dist-packages

Now you can run the program writing from terminal:

cd /usr/lib/python2.6/dist-packages/pyrobot/bin/
./pyrobot

You can also copy the file executable file in your home folder, and write from terminal:

./pyrobot

Roomba's Pyro files configuration

Before starting to use pyrobot with the Roomba, you must modify some settings in the Roomba robot files; these modifications are needed because the interfacing with Roomba that Pyro provides it's general, so there might be different behaviours according to your Roomba model (in this documentation we refer to Roomba 535).

First of all, you need to open the following file, as administrator:

/usr/lib/python2.6/dist-packages/pyrobot/robot/roomba.py

At line 459, you will find the code

dev.sendMsg('\x89\x00\x80\x00\x00')

You should change the last '0' with a '1'. This piece of code represents the bytes sent to the Roomba to force the rotation anticlockwise, but with the final zero, it represents a translation forward.

At line 316, you can find the code which transforms the value related to the rotation angle got by the sensors in radians. You should replace the value '258' with '38.5':

self.sensorData['angleInRadians'] = (2.0 * self.sensorData['rawAngle']) / 258

This depends on how the Roomba sensors process the information, and probably it worked for another model of Roomba. The value 38.5 was obtained by proportions.

At line 318, you have to substitute the value '0.001' with '0.01' in the following line, since the distance run by the Roomba is in centimeters:

d = self.sensorData['distance'] * 0.001

In the end, at line 319, a '-' is needed instead of the '+'

self.x += math.cos(a) * d
self.y += math.sin(a) * d

Wall following algorithm

Here we present the main purpose of this project, a basic wall following algorithm. It makes the Roomba to keep track of the wall on its right using its wall sensor, while the left and right bumper sensors are used to warn the roomba that he hit a wall while going forward.

There are other ways to implement a wall following algorithm, without using the wall sensor, but only the bumper sensors: this kind of implementation would be "blind" and less intelligent, though.

In order to implement the algorithm, we used the finite state machine utility provided by Pyro, that is we classified a pool of possible behaviours of the Roomba and for each of them we implemented a different finite state.

We will describe the main parts of the algorithm.

Importing libraries:

from pyrobot.brain import Brain
from pyrobot.brain.behaviors import State, FSMBrain

These two lines are needed to import from Pyro library the finite state machine and brain structure. They let us to use the algorithm as a brain for the Roomba, using the FSM concepts.


First state: findWall.

class findWall (State):

   def onActivate(self): 
       print "Start"

   def update(self):
       self.left = self.robot.getSensor("leftBump")
       self.right = self.robot.getSensor("rightBump")

       self.robot.move(0.3,0)   
		
       if ((self.right == 1) or (self.left == 1)) :
          self.goto('turnSx')

When the Roomba is in this state (e.g when we start the brain), he will just go straightforward until he finds a wall in front of him. Here the function onActivate(self) just prints out the state in which the robot has just entered (and so it is for the other states as well). The function update(self) is called periodically, while Roomba is in the current state: in this case

class turnSx (State):
 
   def onActivate(self): 
       print "State turnSx"
	
   def update(self):
       self.left = self.robot.getSensor("leftBump")
       self.right = self.robot.getSensor("rightBump")

       self.robot.move(0,0.3)  

       if ((self.right == 0) and (self.left == 0)): 
          self.goto('correctDx')

     
class correctDx (State):

   def onActivate(self):
       print "State correctDx"

   def update(self):
       self.left = self.robot.getSensor("leftBump")
       self.right = self.robot.getSensor("rightBump")
       self.wall = self.robot.getSensor("wallSensor")
 
       self.move(0.25,-0.37)
	
       if ((self.right == 1) or (self.left == 1)) :
          self.goto('turnSx')

       if (self.wall == 1): 
          self.goto('correctSx')


class correctSx (State):
 
   def onActivate(self):
       print "State correctSx"
        
   def update(self):
       self.left = self.robot.getSensor("leftBump")
       self.right = self.robot.getSensor("rightBump")
       self.wall = self.robot.getSensor("wallSensor")
        
       self.move(0.3, 0.15)

       if ((self.right == 1) or (self.left == 1)) : 
          self.goto('turnSx')

       if (self.wall == 0): 
          self.goto('correctDx')
        

def INIT(engine): 
       brain = FSMBrain("Wall Following Brain", engine)
       # add states:
       brain.add(findWall(1))
       brain.add(turnSx())
       brain.add(correctDx())
       brain.add(correctSx())
	
       return brain