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marvin:ecp1 [2009/01/29 00:43] – devamarvin:ecp1 [2009/01/29 09:52] – deva
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 =====Implementation===== =====Implementation=====
 +
 ====Numerical Readings from the Gyroscope==== ====Numerical Readings from the Gyroscope====
 A gyroscope measures degrees per second or simply angle velocity and therefore we must use numerical integration in order to get the angle. A gyroscope has a bias which is simply an offset and naturally we want to eliminate this offset by calibration methods at the beginning of the program. If we do not calibrate, the integration, e.g. a running sum, will add the offset contribution for every sample and diverge resulting in an unstable system. If we calibrate the integral, this problem will be reduced, but unfortunately the bias of the gyroscope tends to drift and therefore one needs to recalibrate if possible or find other heuristic methods in order to avoid an escalation of the error. Sensor fusion is suggested, which rely on the somewhat complex implementation of the Kalman filter, which may combine sensor inputs an re-estimate the most likely bias value while offering an indication of how much trust we may put in this value. A sensor fusion could be used to combine an accelerometer or inclinometer with the gyroscope, but it seems a bit ambitious to implement a Kalman solution given the scope of this project - also keeping in mind that there are many other areas of improvement that should be evaluated first.      A gyroscope measures degrees per second or simply angle velocity and therefore we must use numerical integration in order to get the angle. A gyroscope has a bias which is simply an offset and naturally we want to eliminate this offset by calibration methods at the beginning of the program. If we do not calibrate, the integration, e.g. a running sum, will add the offset contribution for every sample and diverge resulting in an unstable system. If we calibrate the integral, this problem will be reduced, but unfortunately the bias of the gyroscope tends to drift and therefore one needs to recalibrate if possible or find other heuristic methods in order to avoid an escalation of the error. Sensor fusion is suggested, which rely on the somewhat complex implementation of the Kalman filter, which may combine sensor inputs an re-estimate the most likely bias value while offering an indication of how much trust we may put in this value. A sensor fusion could be used to combine an accelerometer or inclinometer with the gyroscope, but it seems a bit ambitious to implement a Kalman solution given the scope of this project - also keeping in mind that there are many other areas of improvement that should be evaluated first.     
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 {{:marvin:projekt_lab1_3.png}}{{:marvin:projekt_lab1_4.png}}  {{:marvin:projekt_lab1_3.png}}{{:marvin:projekt_lab1_4.png}} 
  
-There are many types of algorithms for numerical integrations that are far more advanced and all of them or based on some kind of interpolation technique, but for now we will rely on the simplest form which is the running sum. As an alternative we will implement another solution known as the trapezoidal integration that utilizes a first order polynomial to interpolate between sampled values, hence forming a trapezoidal between every two samples. An illustration of this and further discussion on the topic may be found on wikipidea(([[http://en.wikipedia.org/wiki/Numerical integration|Numerical Integration]])). We will keep the trapezoidal implementation in order to do a comparison between the performance of two methods. +There are many types of algorithms for numerical integrations that are far more advanced and all of them or based on some kind of interpolation technique, but for now we will rely on the simplest form which is the running sum.
- +
-FIXME: trapezoidal integration ???+
  
 ====Programming==== ====Programming====
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 </code> </code>
 The full source code for this class can be found in the file ''GyroscopeSensor.java'' in the Marvin code tarball (([[http://wiki.aasimon.org/lib/exe/fetch.php?media=marvin:marvin-1.0.tar.gz|marvin-1.0.tar.gz]])) The full source code for this class can be found in the file ''GyroscopeSensor.java'' in the Marvin code tarball (([[http://wiki.aasimon.org/lib/exe/fetch.php?media=marvin:marvin-1.0.tar.gz|marvin-1.0.tar.gz]]))
 +
marvin/ecp1.txt · Last modified: 2009/01/29 11:03 by rieper