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Testing, Collecting Data, and Informing Decisions

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Allow students adequate time for testing and logging multiple runs of the robot with its cargo and then answering these questions by reviewing the data they collected. The table for that data is provided below.

Calculating the change in velocity and the change in time

Where will you position the bottle as cargo?

In the Claw Arm Challenge you will be transporting a bottle using the V5 Clawbot. That would be easy but you need to race your robot with its claw arm raised to different heights. So in order to keep your robot from falling over, you will need to decide the best placement of the bottle. Test your assumptions and run some trials with your V5 Clawbot.

Start by investigating the following questions:

  • Where is the V5 Clawbot's center of gravity (CoG)?

  • Does its CoG move when the claw arm is raised? If so, in which direction?

  • How fast can the V5 Clawbot accelerate and remaining stable while its arm is lowered?

  • How fast can the V5 Clawbot accelerate and remain stable while its arm is raised ?

  • Does the position of the bottle affect the V5 Clawbot's stability?

  • Who should be the driver for each of the three rounds?

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The answers to these questions will depend on the data collected about the weight and positions of the bottle, the extension of the claw arm, and the different styles of the drivers. Students' analyses of the data should inform their answers to these questions.

Logging data

Data logging helps you collect the information you need to make a sound, justified decision.

Using the example table below, record your results from at least 9 trial runs. Write the results of each trial in a new row. Fill out each column as follows:

  • Driver: the name of the teammate who drove the robot

  • Bottle Position: the location you placed the bottle on the robot

  • Arm Height: the height the robot's arm was raised

  • Acceleration: the measure of how quickly you accelerated the robot at the start

    • Acceleration = change in velocity / change in time

  • Stayed Upright: yes or no

After you have recorded each trial run, use the results to choose a position for the bottle that lets you accelerate quickly and remain stable.

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The objective of this activity is to have students recognize how basic analyses of logged data (i.e., pattern detection) can inform decisions following systematic testing. Ideally, students should run multiple trials with a control variable technique used in simple experimentation with the robot under different conditions and decide which conditions are best. Students would test each variable with all others held constant so that ultimately, fair comparisons across trials can be made. However, in a classroom setting that might not be possible because exhaustive testing using this method would require many trials. So compromises in the method of testing can be made. No matter the method, students' analyses should highlight the effects of center of gravity, robot and cargo positions, acceleration, and drivers on performances across trials.

Motivate Discussion icon Motivate Discussion

This activity helped students through systematic trial runs of their robots by way of simple experimentation. These questions might help to clarify what the point of this activity was.

Q: Why was this approach better than simply guessing and checking through practice?
A: Human memory of how and why something worked or did not work is limited and prone to error. So log or record the conditions of each trial using organized facts of the data from each trial run so that patterns could be seen. This is better than looking for patterns based on experience alone because biases and memory limitations get in the way of that.

Q: Do you think professionals really test things like this? Why would they?
A: Engineers and scientists systematically test their designs and theories all of the time! They collect and analyze data to support the decisions and plans they make. They do this because often the financial or biological risks are high. They can not risk expensive mistakes to a robot, or much worse to a person's life. Careful testing safeguards against this by making certain plans are based on the facts about the data they have collected.