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...@@ -189,7 +189,7 @@ With the defined parameters the robot is again tested on the aforementioned surf ...@@ -189,7 +189,7 @@ With the defined parameters the robot is again tested on the aforementioned surf
* **Carpet**: The robot could not balance at all * **Carpet**: The robot could not balance at all
* **Wooden table**: The robot could balance for 1-3 seconds * **Wooden table**: The robot could balance for 1-3 seconds
* **White surface**: The robot could balance for ~30 seconds. Ended by falling of the table. * **White surface**: The robot could balance for ~25 seconds. Ended by falling of the table.
Although it seems, from these results, that the color sensor is superior compared to the light sensor analyzed in exercise 1. However the robot configurations in these two exercises are incomparable due to the general robot contruction and the difference in center of gravity. Although it seems, from these results, that the color sensor is superior compared to the light sensor analyzed in exercise 1. However the robot configurations in these two exercises are incomparable due to the general robot contruction and the difference in center of gravity.
...@@ -323,9 +323,13 @@ In the second part, the gyro sensor was attached to the lower part of the robot. ...@@ -323,9 +323,13 @@ In the second part, the gyro sensor was attached to the lower part of the robot.
## Conclusion ## Conclusion
In this lesson we have performed expriements with various robot contructions and software implementation in order to create a self-balancing robot. We found out that this is not a straight forward task and many external factors plays an important role. In this lesson we have performed expriements with various robot contructions and software implementations in order to create a self-balancing robot. We found out that this is not a straight forward task and many external factors plays an important role.
Two sensors, a light and color sensor, was analyzed in a PID control context. With the right surface and the right lighting we were able to make the robot balance for ~25 seconds. However just by introducing natural light the robot was only able to balance for ~2 seconds which is a significantly deterioration. The same applies to the surface where a non-uniform also yields a deterioration. From this we can conclude that when using these types of sensors in a control context the surroundings should be kept in mind.
According to our results the color sensor performed better than the light sensor.
Two sensors,light and color sensor, was analyzed in a PID conrol context.
The self-balancing performance when using the gyro based robot was low. This was mostly due to small errors in angle calculation accumulating over time, causing the robot to loose track of the current angle and then falling over. Since the error in the calculated value was positive in all experiments, we think the sensor may have been more sensitive in the positive direction than the negative. Other sources of error includes the looseness of the construction causing the robot and in turn the gyro measurements to oscillate. We also believe that the motor was introducing tremors, however we did not test this. The self-balancing performance when using the gyro based robot was low. This was mostly due to small errors in angle calculation accumulating over time, causing the robot to loose track of the current angle and then falling over. Since the error in the calculated value was positive in all experiments, we think the sensor may have been more sensitive in the positive direction than the negative. Other sources of error includes the looseness of the construction causing the robot and in turn the gyro measurements to oscillate. We also believe that the motor was introducing tremors, however we did not test this.
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