Autonomous robot's navigation
Hello,
Now I work at associative video memory. The method still in developing (now it version 0.5)
but it gives good results already today.
I am dealing with research of computer vision in parallel with my main job
at "Impulse" more than three years (it is my hobby).
#-Link-Snipped-#
In the beginning my achievements were insignificant and little part of ideas has worked properly.
But I did not surrender. I generated big quantity of hypotheses and then test it.
The most ideas did not work indeed but those that worked were similar to particles of gold
in huge quantity of dross. My associative video memory method is working indeed.
============================- Common information -==========================
Algorithm AVM uses a principle of multilevel decomposition of recognition matrices,
it is steady against noise of the camera and well scaled, simply and quickly
for training, also it shows acceptable quick-action on a greater image resolution
of entrance video (960x720 and more). The algorithm works with grayscale images.
The detailed information about AVM algorithm can be looked here:
#-Link-Snipped-#
AVM SDK v0.5 with examples of using and tests for comparison
of characteristics of the previous and new versions:
#-Link-Snipped-#
Demonstration video how to train AVM:
#-Link-Snipped-#
AVM demo with the user interface (GUI), installation for Windows:
#-Link-Snipped-#
Connect the web-camera and start AVM demo after installation of "Recognition.exe".
After starting the program will inform that there is not stored previously data
of training AVM and then will propose to establish the key size of the image
for creation of new copy AVM. Further train AVM using as an example Face_training_demo.avi.
========================- Robot's navigation -=========================
I also want to introduce my first experience in robot's navigation powered by AVM.
Briefly, the navigation algorithm do attempts to align position of a tower
and the body of robot on the center of the first recognized object in the list
of tracking and if the object is far will come nearer and if it is too close it
will be rolled away back.
See video below:
<a href="https://www.youtube.com/watch?v=EcXl0mQcPxs" target="_blank" rel="nofollow noopener noreferrer">Robot's navigation by computer vision (AVM algorithm), experiment 1 - YouTube</a>
<a href="https://www.youtube.com/watch?v=AvQMi_DdF94" target="_blank" rel="nofollow noopener noreferrer">Robot's navigation by computer vision (AVM algorithm), experiment 2 - YouTube</a>
I have made changes in algorithm of the robot's control
also I have used low resolution of entrance images 320x240 pixels.
And it gave good result (see "Follow me"):
<a href="https://www.youtube.com/watch?v=HTxNlOpm11U" target="_blank" rel="nofollow noopener noreferrer">Follow me (www.edv-detail.narod.ru) - YouTube</a>
Robot navigation by gate from point "A" to "B"
See video below:
<a href="https://www.youtube.com/watch?v=R4Ogq2YUfrU" target="_blank" rel="nofollow noopener noreferrer">- YouTube</a>
<a href="https://www.youtube.com/watch?v=1w2bMlTsLUI" target="_blank" rel="nofollow noopener noreferrer">- YouTube</a>
First an user must set the visual beacons (gates) that will show direction where robot has to go.
Robot will walk from gate to gate. If the robot recognize "target" then he come nearer and stop walking.
Navigation application (installation for Windows):
#-Link-Snipped-#
Installation also contains source code of robot control driver (InstDir\RobotController_src).
So you can adapt it to your robots and repeat my navigation experiments by yourself.
Also available source code of navigation application (but all comments in Russian):
#-Link-Snipped-#
Source code of "Navigator" program was prepared for Russian community.
But if you would show interest to this topic then I could adapt it to English community too.
Now I work at associative video memory. The method still in developing (now it version 0.5)
but it gives good results already today.
I am dealing with research of computer vision in parallel with my main job
at "Impulse" more than three years (it is my hobby).
#-Link-Snipped-#
In the beginning my achievements were insignificant and little part of ideas has worked properly.
But I did not surrender. I generated big quantity of hypotheses and then test it.
The most ideas did not work indeed but those that worked were similar to particles of gold
in huge quantity of dross. My associative video memory method is working indeed.
============================- Common information -==========================
Algorithm AVM uses a principle of multilevel decomposition of recognition matrices,
it is steady against noise of the camera and well scaled, simply and quickly
for training, also it shows acceptable quick-action on a greater image resolution
of entrance video (960x720 and more). The algorithm works with grayscale images.
The detailed information about AVM algorithm can be looked here:
#-Link-Snipped-#
AVM SDK v0.5 with examples of using and tests for comparison
of characteristics of the previous and new versions:
#-Link-Snipped-#
Demonstration video how to train AVM:
#-Link-Snipped-#
AVM demo with the user interface (GUI), installation for Windows:
#-Link-Snipped-#
Connect the web-camera and start AVM demo after installation of "Recognition.exe".
After starting the program will inform that there is not stored previously data
of training AVM and then will propose to establish the key size of the image
for creation of new copy AVM. Further train AVM using as an example Face_training_demo.avi.
========================- Robot's navigation -=========================
I also want to introduce my first experience in robot's navigation powered by AVM.
Briefly, the navigation algorithm do attempts to align position of a tower
and the body of robot on the center of the first recognized object in the list
of tracking and if the object is far will come nearer and if it is too close it
will be rolled away back.
See video below:
<a href="https://www.youtube.com/watch?v=EcXl0mQcPxs" target="_blank" rel="nofollow noopener noreferrer">Robot's navigation by computer vision (AVM algorithm), experiment 1 - YouTube</a>
<a href="https://www.youtube.com/watch?v=AvQMi_DdF94" target="_blank" rel="nofollow noopener noreferrer">Robot's navigation by computer vision (AVM algorithm), experiment 2 - YouTube</a>
I have made changes in algorithm of the robot's control
also I have used low resolution of entrance images 320x240 pixels.
And it gave good result (see "Follow me"):
<a href="https://www.youtube.com/watch?v=HTxNlOpm11U" target="_blank" rel="nofollow noopener noreferrer">Follow me (www.edv-detail.narod.ru) - YouTube</a>
Robot navigation by gate from point "A" to "B"
See video below:
<a href="https://www.youtube.com/watch?v=R4Ogq2YUfrU" target="_blank" rel="nofollow noopener noreferrer">- YouTube</a>
<a href="https://www.youtube.com/watch?v=1w2bMlTsLUI" target="_blank" rel="nofollow noopener noreferrer">- YouTube</a>
First an user must set the visual beacons (gates) that will show direction where robot has to go.
Robot will walk from gate to gate. If the robot recognize "target" then he come nearer and stop walking.
Navigation application (installation for Windows):
#-Link-Snipped-#
Installation also contains source code of robot control driver (InstDir\RobotController_src).
So you can adapt it to your robots and repeat my navigation experiments by yourself.
Also available source code of navigation application (but all comments in Russian):
#-Link-Snipped-#
Source code of "Navigator" program was prepared for Russian community.
But if you would show interest to this topic then I could adapt it to English community too.
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