Author Topic: RoboCup Humanoid League  (Read 2989 times)

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Offline DelTopic starter

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RoboCup Humanoid League
« on: July 10, 2007, 01:32:42 PM »

Offline Camtron

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Re: RoboCup Humanoid League
« Reply #1 on: July 10, 2007, 07:12:08 PM »
I just watched the video, are the bots remote or autonomous.  I think this would be a blast to watch.

Offline Poolrad

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Re: RoboCup Humanoid League
« Reply #2 on: July 10, 2007, 07:26:07 PM »
They appear to be autonomous, I saw no one using remotes anywhere.

Offline KambeiX

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Re: RoboCup Humanoid League
« Reply #3 on: July 10, 2007, 07:34:33 PM »
They seem autonomous but I think they can talk to each other.

Offline Admin

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Re: RoboCup Humanoid League
« Reply #4 on: July 11, 2007, 07:55:14 PM »
They are semi-autonmous . . .

How do I know? Well, in the beginning a guy yelled out 'no flash photography.' This is because it messes up cameras . . .

I dont see any sensors on the robots, so Im assuming there is an overhead cam linked to a PC that is wireless controlling the robots. This is how they normally do it in robocup . . .

Offline JonHylands

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Re: RoboCup Humanoid League
« Reply #5 on: July 11, 2007, 08:08:56 PM »
No, the cameras are mounted on the head. These robots are fully autonomous...

- Jon

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Re: RoboCup Humanoid League
« Reply #6 on: July 11, 2007, 09:13:42 PM »
I spoke too soon . . .
http://www.nimbro.net/papers/Humanoids06_Behnke.pdf

Quote
B. Visual Object Detection
The only source of information about the environment of our
robots is their camera. The wide field of view of the camera(s)
allows them to see at the same time their own feet and objects
above the horizon. Figure 3 shows on the left two images
captured from the perspective of a robot on the field.
Our computer vision software detects the ball, the goals, the
corner poles, the field lines, and other players based on their
color. The FlyCam captures RGB images with a resolution
of 320?240 pixels. The images are converted into the YUV
color space to decrease the influence of different lighting
conditions. The forward camera directly delivers YUV 4:2:2
images. Using a look-up table, the colors of individual pixels
are classified into color-classes that are described by ellipsoids
in the UV-plane. We correct for the average brightness and for
the darkening of the lens towards the periphery. In a multistage
process we discard insignificant colored pixels and detect
colored objects. We estimate their coordinates in an egocentric
frame (distance to the robot and angle to its orientation), based
on the inverted projective function of the camera. We correct
first for the lens distortion and invert next the affine projection
from the ground plane to the camera plane. The estimated
egocentric coordinates of the key objects are illustrated in the
upper right part of Fig. 3. Here, the objects detected by both
cameras are fused, based on their confidence.


The paper continues on about other things such as self localization, behaviors, gaits, etc.

Fairly impressive robots!

 


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