Tonight we had a group meeting. We mainly clarified things about the Kalman filter. Here is what we understood so far.

The Kalman filter is a recursive filter which is going to help us predict the state of our robot with accuracy. There are two phases in the algorithm. During the first phase, we use the previous estimates of the state and of the error to predict an a priori state estimate and an a priori error estimate. In the second phase we calculate the Kalman gain based on this a priori error. Then we use the measures from the captors to modify our a priori estimation and thus reach our final estimation. The Kalman gain previously calculated determines how much we weight the measure from the captors compared to the predictions.

Now that we understood it, we have a to work on the modelisation of out system before we can implement it. There will be two parts in our servo control: the first one will keep the board horizontal, the second one will control the motion. For every case we will use the Kalman filter and the variables controled will be the angles.



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