For this project, we tested the range of the light sensor on various colors. We used ten different colors, ranging in brightness. To perform our tests, we put our sensor at the end of a tape measure, and at increments of a half inch, we noted what the sensor was reading for each specific color. In the picture of our setup you can see that we have one of the motors on the wire. This was to weigh the wire down, because if we did not do this the light sensor would have looked directly at the floor. Our results were definitely interesting.
            One thing we noticed about our results is that the accuracy of the light sensor was minimal. At a half inch, each color had a different number to correspond with how much light was reflected back to the sensor. However, almost immediately as we moved it to the one-inch mark, nearly every color had some distortion of the reading. The only colors that did not have a different reading at one inch than half an inch were the two lighter blues, red, and navy blue. What’s even more interesting is that white showed a reading of 0 at one inch, meaning that the light sensor wasn’t recognizing anything was there at all, and yet there was a reading at 1.5-3 inches. Regardless, every color dropped down to one for a period of time, regardless of wat their original number was. This shows that the light sensor’s range for being able to identify unique colors is very short. Finally, the light sensor detected the brighter colors for a longer distance than the darker colors. It detected white and light blue for 3 inches, but only saw navy blue for an inch, and black at a mere half an inch.
            It must also be noted that these reading are with semi-translucent material. If this material was opaque, it would probably detect these colors more accurately at a further distance. But, because some light was able to pass through, the light sensor had a very short range.
            Recently, we discussed self-automated vehicles during lecture. Currently, these cars use light and ranging sensors to identify curbs and changing lights. It was very interesting in that Google and Tesla cars use similar features to identify color of cars, vehicles, and roads just like our tests did with the EV3.