@@ -228,9 +228,7 @@ Ved måling ved start-hvile-position: skriv at vi bruger teoretisk setpoint (i.e
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@@ -228,9 +228,7 @@ Ved måling ved start-hvile-position: skriv at vi bruger teoretisk setpoint (i.e
*Figure 5: Plot of readings from color sensor (orange) and light sensor (brown) along with their setpoints*
*Figure 5: Plot of readings from color sensor (orange) and light sensor (brown) along with their setpoints*
... describe graphs...
The two graphs have about the same shape, with two peaks separated by a valley. The first ~800 ms is the calibration of the setpoint. Then we see the reading values rise as the robot is tilted to its starting position, causing the sensor to get closer and closer to the surface below. We are not sure as to the reason for the occurence of the valleys - a guess could be that the robot is lifted slightly from the table again because Camilla, who tilting the robot, was wary of letting the sensor hit the surface too hard. The very different depths of the valleys could be due to the change in lighting level, as described previously.
The two graphs have about the same shape, with two peaks separated by a valley. The first ~800 ms is the calibration of the setpoint. Then we see the reading values rise as the robot is tilted to its starting position, causing the sensor to get closer and closer to the surface below.
(skriv om at dal nok er større ved den ene pga. anderledes lysforhold)
We see from the plot that the color sensor actually has a larger maximum deviation from the setpoint than the light sensor. The color sensor has a larger average deviation as well. This goes against our immediate visual observations. On the other hand, the slope of the graph for the color sensor is less steep than that of the light sensor, which might explain our observations. The different slopes, however, could also be due to different speeds of the movement when tilting the robot - it should be noted, though, that this could also have affected our results in a way that diminishes the actual difference which might support our observations.
We see from the plot that the color sensor actually has a larger maximum deviation from the setpoint than the light sensor. The color sensor has a larger average deviation as well. This goes against our immediate visual observations. On the other hand, the slope of the graph for the color sensor is less steep than that of the light sensor, which might explain our observations. The different slopes, however, could also be due to different speeds of the movement when tilting the robot - it should be noted, though, that this could also have affected our results in a way that diminishes the actual difference which might support our observations.
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@@ -253,6 +251,8 @@ Afterwards we modified the Gyrotest to let both motors run while the program ran
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@@ -253,6 +251,8 @@ Afterwards we modified the Gyrotest to let both motors run while the program ran
Average reading with wheels on: 596.698744 (No noticeable average drift)
Average reading with wheels on: 596.698744 (No noticeable average drift)
Average reading without wheels: 596.5979995 (No noticeable average drift)
Average reading without wheels: 596.5979995 (No noticeable average drift)
(KOMMENTAR: Skriv med at de to average readings ikke er særligt forskellige)
Based on the code in [Ref or lesson plan], we tried implementing integration to calculate the angle of the gyro based on the sample interval and the gyro motion readings. The result was however extremely inaccurate, and would for some reason slowly slide in one direction, even when the gyro was motionless. We assumed this was a result of the constant small speed readings made by the gyro even when still, but these should work in both directions as the offset remains the same despite fluctuations to either side, so the drifting angle is a mystery.
Based on the code in [Ref or lesson plan], we tried implementing integration to calculate the angle of the gyro based on the sample interval and the gyro motion readings. The result was however extremely inaccurate, and would for some reason slowly slide in one direction, even when the gyro was motionless. We assumed this was a result of the constant small speed readings made by the gyro even when still, but these should work in both directions as the offset remains the same despite fluctuations to either side, so the drifting angle is a mystery.
No we would have attempted to fix the inaccurate angle calculation and then use this as a means of creating a balancing robot using the gyro, but at this point we simply spent too much time on these exercises and have to cut our gyro implementation short and leave it at this.
No we would have attempted to fix the inaccurate angle calculation and then use this as a means of creating a balancing robot using the gyro, but at this point we simply spent too much time on these exercises and have to cut our gyro implementation short and leave it at this.
