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Unread 04-12-2010, 11:54 PM
Radical Pi Radical Pi is offline
Putting the Jumper in the Bumper
AKA: Ian Thompson
FRC #0639 (Code Red Robotics)
Team Role: Programmer
 
Join Date: Jan 2010
Rookie Year: 2010
Location: New York
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Re: IR Scanning Method Of Object Recognition

Let's see if I have this right (see attached image as well). We know one leg, the relative height of the camera. The IR system sends out the beam across everything in LoS. The camera picks up on this, and determines the angle of the object from the camera (simple y-coordinate conversion). Do a bit of trig, and we end up with the length of the IR beam; the distance to the object from the robot.

Assuming that's your idea, we can expand on this a bit more. We not only have the Y-coordinates of the object in the camera, but also the X. With the X, we can calculate the angle of the object from the camera, giving us 2D vectors of the object.

Honestly, I think the entire 3D idea is a bit over the top. There are only a few things to expect on the field at any point in the game: balls, robots, and static field elements. With such a small sample of things to expect, it shouldn't be that hard to differentiate between those things (static elements will span large areas, balls will be relatively small (IR reflection length), and robots can easily be detected by the camera because of the GDC's convenient bumper requirements. A 2D field is plenty for the AI to work with, and in some ways easier too. It's not like we're ever going to be able to track balls in the air in real-time.

I see a few problems though. Because of the camera's limited FoV combined with physical obstructions such as the bumpers, there are going to be areas that the camera cannot see objects in front of it, breaking this entire system. However, one could argue that those areas are right up next to the robot, and possibly could even be detected by the lack of any IR beam in the areas it covers. Being so close to the robot, the plotting becomes less useful compared to just knowing it is there.

Also, as the object gets farther away, the change in angle for a given unit curves to eventually near-zero, and the accuracy of the reading will follow the trend too. Since the cameras are obviously not perfect in their detection, longer-distance objects will most likely start to return erratic values, diminishing the system's usefulness.
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