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Re: Finding where you are in the field
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Basically, if you look at each of the sensors available to us, they are all useful for localization but none of them is perfect: * Gyros/Accelerometers: Very fast response and good accuracy initially, but by integrating accelerations and velocities over time, they drift. * Encoders: Very precise distance/speed measurements...as long as your wheels don't slip and you know the precise diameter of your wheels. * Vision system: Seeing a known "landmark" like the goal tells you a great deal about your absolute position on the field, but you can't always see the goal, and you will sometimes get false alarms depending on the environment. In robotics, we often find robots with this arrangement of sensors. By fusing their outputs together, you can get a system that compensates for the individual failings of each sensor. For example, you might use your gyro for most of your heading measurements, but if you get a good shot of the goal, you "reset" your gyro to reduce/eliminate drift. Common fusion techniques include Extended and Unscented Kalman Filters. Unfortunately, getting these systems working is a Masters/PhD level challenge, and would be difficult to get working well in 6 weeks for anyone (especially since you won't have a testable robot for much of that time). That said, I am hoping that at least a few ultra high end teams take on this challenge (I'm looking at you, 254). Jared |
Re: Finding where you are in the field
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Even though it doesn't sound difficult, it is very tough to do in practise. |
Re: Finding where you are in the field
I think the group of people here who believe that they can rely soly on the vision target that is significantly behind the basket do not understand what happens when you move to either side rather than shooting straight on. THAT is why field awareness will be usefull, unless you plan on shooting only from the key.
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