... | ... | @@ -165,9 +165,11 @@ In the code that we used as basis for our own, ***p*** was 28. |
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Using the grid search approach again, we first tried with 40 (robot falling forward a lot) and 10 (robot corrects too slowly but simply falls forward right away). We then tried 70, in which case the robot also kept falling a lot, but the corrections seemed more aggressive (which makes sense), causing it to oscillate violently.
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We changed the setpoint to 585 and tried values of p between 40 and 70. As before, 70 caused violent oscillations and instability. Both 55 and 40 seemed more stable. We decided to continue testing with a value of 40.
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Additional observation: The robot seems to stop briefly once in a while. This seemed to happen more with p = 70 than with p = 40.
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Additional observation: The robot seems to stop briefly once in a while, usually causing the robot to instantly fall over. This seemed to happen more with p = 70 than with p = 40, and can be seen in video 4.
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NOTE (Camilla): Måske skal vi have en video for ovenstående additional observation?
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[![Robot motors randomly stopping](http://img.youtube.com/vi/ZMNerUoDBZE/0.jpg)](https://www.youtube.com/watch?v=ZMNerUoDBZE)
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*Video 4: Robot motors randomly dead stopping, causing it to fall*
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*I value:*
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... | ... | @@ -259,6 +261,9 @@ We would have attempted to amend the inaccurate angle calculation and then use t |
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## Conclusion
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(TODO alle: SKRIV MERE KONKLUSION TIL LIGHT SENSOR)
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(Nicolai: Wrote some, ved ikke om det er fint)
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Our experimentation with creating a balancing robot using the light sensor proved to be immensely difficult. Systematically attempting different values for our P, I, D and setpoint variables proved somewhat fruitful, as we managed to somewhat remedy some of the issues that appeared to be causing the robot to fall over, but in the end it was still unable to balance for more than a second or two at best. A big culprit leading to this was also the mysterious random stopping of the motors that we were unable to find the source of.
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Testing the parameters in a way inspired by grid search provided us with a structured approach to investigating how the different paramters influence each other. Although limited time forced us to move on before fully completing an actual grid search, we did obtain some insights into the effect of the parameters on the robot's responsiveness to the light sensor.
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