... | ... | @@ -169,28 +169,25 @@ An additional observation that we made is that the robot seemed to stop briefly |
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*Video 4: Robot motors randomly stopping dead, causing it to fall*
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##### I value
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We observed that once the robot started falling in any direction (forwards or backwards) it would generally keep slowly falling in that direction until tipping over (despite also driving in that direction in an attempt to balance). As such, we needed a stronger reaction on past accumulated errors, which is exactly what the integral variable controls, so we experimented with different I-values.
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We observed that once the robot started falling (forwards as well as backwards) it would generally keep slowly falling in that direction until tipping over (despite also driving in that direction in an attempt to balance). As such, we needed a stronger reaction on past accumulated errors, which is exactly what the integral variable controls, so we experimented with different *i*-values.
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Initially the I-value was 4, and as just explained we needed a stronger reaction on past errors, so we attempted increasing it.
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Initially the *i*-value was 4, and as just explained we needed a stronger reaction on past errors, so we tried increasing it.
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| Integral Value | Status | Observation |
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| --- | --- | --- |
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| Integral Value | Status | Observation |
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| --- | --- | --- |
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| 4 | Poor | Keeps slowly falling |
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| 5 | Slightly better | Keeps slowly falling |
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| 6 | Better | Usually manages to correct aggressively enough |
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| 7 | Possibly better | Looks similar to 6, hard to tell if improvement|
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| 8 | Worse | Reacts too violently often ends up falling to the opposite side |
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After increasing the I-value to 6 or 7, the robot appears to react more violently to steadily increasing errors and as such prevents the slow-but-certain fall behavior it previously had. However, it ultimately still tips over to either side rather quickly.
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After increasing the *i*-value to 6 or 7, the robot appeared to react more violently to steadily increasing errors and thereby prevented the slow-but-certain fall behavior it previously had. However, it ultimately still tipped over to either side rather quickly.
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We decided to finish up the experiments (long) before having tried all possible combinations of parameters values, as it seemed pointless to go on when seeing no significant improvements to the robot's behavior - continuing would not improve on our understanding of the effects of the different PID parameters further. However, the parameter search inspired by grid search did provide us with a more structured insight into the effects of the different parameters than had we just tried varying each parameter on a whim.
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We decided to finish up the experiments (long) before having tried all possible combinations of parameters values, as it seemed pointless to go on when seeing no significant improvements to the robot's behavior - continuing would not improve on our understanding of the effects of the different PID parameters further. However, the parameter search inspired by grid search did provide us with a more structured insight into the effects of the different parameters than had we just tried varying each parameter on a whim.
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### Self balancing robot with color sensor
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---
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We replaced the light sensor with a color sensor and did some trial runs with the robot to see how well it performed on the basic settings (p = 28, i = 4, d = 33). These initial runs led to the following interesting observation:
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NOTE (Camilla): Havde vi ikke ændret til værdierne fra forrige eksperiment? Altså p = 40, i=6 ?)
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NOTE (Nicolai): Fwuak, kan jeg ikke huske...
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We replaced the light sensor with a color sensor and did some trial runs with the robot to see how well it performed on settings decided from the previous experiment: p = 40, i = 6, d = 33. These initial runs led to the following interesting observation:
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Changes in the robot's position seemed to be reflected less strongly in the readings of the color sensor than those of the light sensor - that is, the same angle seemed to induce less of a change in the color sensor's readings. We therefore speculated that we needed to multiply the error by a larger value in order to adequately correct the robot's position, which means that a larger value of *p* might result in the robot balancing better as the change in reading would then have a larger effect. We tested this by trying out different values of ***p***, using the same *p*-values as when testing the light sensor. We weren't able to observe any improvements for certain and did not press on with this approach. Instead, we decided to compare the two types of sensor more closely.
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