Arduino code techniques - Versionsgeschichte

Alexanderg: /* Median filter */

2015-06-29T11:20:33Z

‎Median filter

2015-06-04T21:00:46Z

‎Mean filter

2014-11-11T11:45:19Z

‎Further links

2014-11-11T11:41:36Z

‎Time constant

2014-11-11T11:38:26Z

‎Time constant

2014-11-11T11:37:48Z

‎High pass filter

2014-11-11T11:35:25Z

‎Integration

2014-11-11T11:34:36Z

‎Low pass filter

2014-09-13T17:14:35Z

‎Low pass filter

2014-09-13T17:12:29Z

‎Low pass filter

@@ Zeile 78: / Zeile 78: @@
      return (values[count/2]); // Return the median.
+  }
 =Further links=
 http://robottini.altervista.org/tag/complementary-filter

@@ Zeile 55: / Zeile 55: @@
 This defines where the boundary between trusting the gyroscope and trusting the accelerometer is. For time periods shorter than half a second, the gyroscope integration takes precedence and the noisy horizontal accelerations are filtered out. For time periods longer than half a second, the accelerometer average is given more weighting than the gyroscope, which may have drifted by this point.
-=Mean filter=
+=Median filter=
 *Problem: you have multiple ADC measurements (battery, ultrasonic etc.) that can contain outliers. What value is the 'most seen value'?
 *Solution: This code will sort and choose the center value.
@@ Zeile 78: / Zeile 78: @@
      return (values[count/2]); // Return the median.
+  }
 =Further links=
 http://robottini.altervista.org/tag/complementary-filter

← Nächstältere Version		Version vom 11. November 2014, 11:45 Uhr
Zeile 81:		Zeile 81:

	=Further links=		=Further links=
		+
		+	http://robottini.altervista.org/tag/complementary-filter

← Nächstältere Version		Version vom 11. November 2014, 11:41 Uhr
Zeile 40:		Zeile 40:

	So if you know the desired time constant and the sample rate, you can pick the filter coefficient 'a'.		So if you know the desired time constant and the sample rate, you can pick the filter coefficient 'a'.
		+
		+
		+	=Complementary filter=
		+
		+	This just means the two parts of the filter always add to one, so that the output is an accurate, linear estimate in units that make sense. The filter presented here is not exactly complementary, but is a very good approximation when the time constant is much longer than the sample rate (a necessary condition of digital control anyway).
		+
		+	angle = (0.98)(angle + gyrodt) + (0.02)*(x_acc);
		+
		+	If this filter were running in a loop that executes 100 times per second, the time constant for
		+	both the low-pass and the high-pass filter would be:
		+
		+	tau = adt/(1-a) = 0.980.01sec/0.02 = 0.49sec
		+
		+	This defines where the boundary between trusting the gyroscope and trusting the accelerometer is. For time periods shorter than half a second, the gyroscope integration takes precedence and the noisy horizontal accelerations are filtered out. For time periods longer than half a second, the accelerometer average is given more weighting than the gyroscope, which may have drifted by this point.

	=Mean filter=		=Mean filter=

@@ Zeile 35: / Zeile 35: @@
   y = (a)*(y) + (1-a)*(x);
-running in a loop with sample period, dt, can be found like this:
+running in a loop with sample period 'dt', can be found like this:
   tau = a * dt / (1-a)    <=>     a = tau / (tau+dt)

@@ Zeile 26: / Zeile 26: @@
 =High pass filter=
 It allows short-duration signals to pass through while filtering out signals that are steady over time. This can be used to cancel out drift.
 =Mean filter=

← Nächstältere Version		Version vom 11. November 2014, 11:35 Uhr
Zeile 2:		Zeile 2:

	=Integration=		=Integration=
−	~~position += speed * dt;~~
−	~~angle += gyro * dt;~~

		+	To get the new position at each tick, you take the old position and add the change in position. The change in position is just the
		+	speed of the robot multiplied by the time since the last tick, which you can get from the timers on the microcontroller or some other known timer.
		+
		+	position += speed * dt;
		+	angle += gyro * dt;

	=Low pass filter=		=Low pass filter=

@@ Zeile 1: / Zeile 1: @@
 This page shows example code, common code techniques etc. that can be useful for your own programming experiments with the Arduino.
 =Low pass filter=
-*Problem: you have multiple ADC measurements (battery, ultrasonic etc.) that can quickly change. What value is the 'smooth value' (low-pass filter)?
+*Problem: you have multiple ADC measurements (battery, ultrasonic etc.) that can quickly change. What value is the 'smooth value' (low-pass filter)? The goal of the low-pass filter is to only let through long-term changes, filtering out short-term fluctuations.
 *Solution: This code will apply a 'low-pass' filter to your values. A 'weight' constant specifies the cut-off frequency, so how much your values are 'smoothed': if the weight is lower, the cutt-off frequency is lower (so more is filtered out), however the more slowly the system adapts to the last measurements.
@@ Zeile 12: / Zeile 17: @@
     Serial.println(value); // print value
+  }
 =Mean filter=

@@ Zeile 3: / Zeile 3: @@
 =Low pass filter=
 *Problem: you have multiple ADC measurements (battery, ultrasonic etc.) that can quickly change. What value is the 'smooth value' (low-pass filter)?
-*Solution: This code will apply a 'low-pass' filter to your values. A 'weight' constant specifies the cut-off frequency, so how much your values are 'smoothed'. If the weight is higher, the cutt-off frequency is lower (so more is filtered out), however the more slowly the system adapts to the last measurements.
+*Solution: This code will apply a 'low-pass' filter to your values. A 'weight' constant specifies the cut-off frequency, so how much your values are 'smoothed': if the weight is lower, the cutt-off frequency is lower (so more is filtered out), however the more slowly the system adapts to the last measurements.
   float value = 0.0;
-  float weight = 0.9; // how much your values are 'smoothed'
+  float weight = 0.1; // how much your values are 'smoothed'
   void loop(){