@@ -88,19 +88,19 @@ We tried to change the i-value - which was 4 initialy
...
@@ -88,19 +88,19 @@ We tried to change the i-value - which was 4 initialy
7: - maybe better
7: - maybe better
8: poorer
8: poorer
*Setpoint:*
*Setpoint:*
We tried a form of grid search on the value of the setpoint: Noting that the value measured by the robot initially was around 580, we started with a value of 500 to see the effect. We then tried a value of 750 (simply to try something somewhat in the middle between 500 and the highest possible value of 1000-something). Seeing how poorly both of these affected the robot's performance, we then tried a value of 600 which made the robot perform a lot better. We then started narrowing in between 600 and 550, ending at a value of 577.
We tried a form of grid search on the value of the setpoint: Noting that the value measured by the robot initially was around 580, we started with a value of 500 to see the effect. We then tried a value of 750 (simply to try something somewhat in the middle between 500 and the highest possible value of 1000-something). Seeing how poorly both of these affected the robot's performance, we then tried a value of 600 which made the robot perform a lot better. We then started narrowing in between 600 and 550, ending at a value of 577.
NB: The robot was casting shade, depending on the direction that the light was coming from, which affected the light readings.
NB: The robot was casting shade, depending on the direction that the light was coming from, which affected the light readings.
*P value:*
*P value:*
Was originally 28, in the code that we used as basis for our own.
Was originally 28, in the code that we used as basis for our own.
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.
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.
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.
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.
Additional observation: The robot seems to stop briefly once in a while. This seemed to happen more with p = 70 than with p = 40.
Additional observation: The robot seems to stop briefly once in a while. This seemed to happen more with p = 70 than with p = 40.
*TRYING SOMETHING NEW (inspired by the above approach)*
*TRYING SOMETHING NEW (inspired by the above approach)*
Strategy: Trying extreme values for each variable to understand the effect of changing it. Then trying empirically to find good parameter values by utilizing this knowledge.
Strategy: Trying extreme values for each variable to understand the effect of changing it. Then trying empirically to find good parameter values by utilizing this knowledge.
