Gabe noisy light #40
Open
Gabe noisy light #40
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
… different bots. I then analyzed this to find the distribution of each of these colors. I found that the standard deviation of the light measurements depended on the intensity of the light being measured (more light->higher standard deviation). Fit a second degree polynomial to the measurements I took. Thus, each time get_ambientlight() is called, the standard deviation polynomial uses the noise-free measurement to find the std_dev at that measurement and a normal distribution random number generator finds the measurement with noise.
Owner
|
That's so overkill. I love it. I'm going to wait to merge this until we figure out the multi-threading issue, in case the problem is related to the threaded random number generation. |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
For issue #19: Took 50 measurements of 3 different colors (white, grey, black) on 15 different real Kilobots. Then analyzed the measurements to find the distribution of each of these colors. I found that the standard deviation of the light measurements depends on the intensity of the light being measured (more light->higher standard deviation). I then fit a second-degree polynomial to the std_dev of the measurements I took. Each time get_ambientLight() is called, the standard deviation polynomial uses the noise-free measurement to find the std_dev at that specific measurement. A normal distribution random number generator finds the measurement with noise.