CART ON RAMP LAB
9/23/21
This is the Cart on Ramp Lab, a lab where we let a buggy run down a ramp and measured the the speed of it in each second using 30fps video.
Overview
Research Question: How does time effect the position of our Buggy?
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Independent Variable: The time the Buggy was allowed to traverse
Dependent Variable: The final position of our Buggy
Control Variable: The Slope of the ramp used by the Buggy
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The Controlled Variables
To keep this experiment Controlled, we used the same ramp and the same Buggy in out trials, as well as keeping the slope the same (above) to avoid drastic inaccuracies in our results.
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Collection of Data
To collect our data we used a smartphone filming in 4k @30fps. After inserting a meter stick into the frame, this allowed us to collect data based on how many pixels the buggy traversed in one frame, yielding accurate results in the process.
Meet The Team
Procedure
- Firstly we set the Buggy up on a ramp with measurement markings in centimetres along side it, the buggy was placed at the top of the ramp and pointed downwards. We also placed a meter stick next to the ramp as reference for video analysis.
- We started to film 3 seconds before the Buggy was release from its initial position, we then released the Buggy and caught the events on film.
- We collected the Buggy when it rolled down the ramp.
- The video recording was sent to each individual member's computers and imported into Logger Pro for video analysis.
- In Logger Pro we broke the video down into its individual frames, clicking and measuring the change in position in each frame (every 1/30th of a second). Details on how to do this can be found in the pdf packet embedded below.
- We preformed one trial of this experiment
Lab Setup
Ramp
Buggy
Meter Stick
Moon
Phone/Camera
Raw Data
Better quality CSV file containing all raw data is also embedded below
Processed Data
For our experiment there wasn't a need particularly to process the Raw Data that we collected, as our data went directly from the collection process into a graphical representation. We were able to create two graphs using our data set: a position-time graph and a velocity-time graph (below)
The position-time graph (above) shows the direct result of factoring our raw data into a XY plot, where the X-axis represents distance in meters and the Y-axis (or T) represents time in seconds. Note that our position follows a negative correlation but is not an error. This is because of the direction that our buggy was travelling at the time, going from right to left on the screen, giving Logger Pro incentive to think that our Buggy was moving backwards.
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For this experiment we went for a quadratic fit for the data (y = ax^2 + bx + c), taking into account that the buggy was not travelling at a constant speed but accelerating as it went along. using the data from above we were also able to create a velocity-time graph.
The velocity-time graph (above) depicts the velocity of the buggy (in m/s) at any given time of the video. as mentioned above, the negative correlation of the graph is not an error and was simply the interpretation of the data by Logger Pro. Unlike the position-time graph, this graph showed a linear fit. This makes sense as an acceleration of the buggy would mean a increase in velocity over time, yielding a slope for the graph, whereas no acceleration would yield somewhat of a line for the graph. Our graph had a slope of around 1, meaning that every second the velocity would increase by approximately 1 m/s.
Downloads of the two graphs are linked below
The purpose of this lab was to calculate the position of the buggy as it ran down the slope. As shown above, we were able to do this by using video analysis to pinpoint the individual locations of the buggy at specific periods of time. This then translated into the two graphs shown above, the position-time graph and the velocity-time graph. We were able to use these results to draw conclusions such as the buggy was indeed accelerating down the ramp (slope of velocity-time graph). The results of this lab can be generalised to other situations of find the acceleration of an object, as well as the change in position and velocity over time.
Processed Data
Conclusion
In conclusion, we can say the results in this lab can be interpreted by saying that, since the buggy was not moving at a constant velocity, the buggy was accelerating down the ramp. A quantitative representation of the acceleration can also be derived from the velocity-time graph by looking at the slope. From this data, we can say that our Buggy's rate over acceleration was close to -1 m/s/s. Due to the fact that we only did video analysis on one sample in this experiment, we can say that we are moderately confident in our results. The collection of more points and further video analysis on samples would have improved our confidence had we the time. Some limitations and uncertainties as well as resolutions to these will be discussed below.
Conclusion
Uncertainties and Limitations
Lining up the position of the origin as well as clicking the individual points in video could lead to minor uncertainties regarding accuracy.
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We only used one video for our experiment, locking in our specific results to that specific sample, limiting variation and increasing the possibility of error.
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Improvements? - taking more trails and doing video analysis on those samples could greatly improve both uncertainties mentioned, as collecting more data negates the considerations of minor errors in measurement in numbers, and more trials improve the accuracy of our measurements in general.