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1. The Problem We Want to Solve
Every day, students spend many hours inside classrooms. The air they breathe is an important part of their learning environment, especially for students with asthma, allergies, and other respiratory conditions.
Poor indoor air quality can contribute to respiratory irritation and can make certain health conditions worse. Possible environmental concerns include airborne particles, dust, smoke, and excessive moisture.
In New Mexico, outdoor air pollution and wildfire smoke can also affect indoor environments.
However, students and teachers usually cannot see how air conditions vary throughout their classrooms.
Our team wants to investigate whether robotics and computer science can help collect and understand environmental information.
We believe that students can use technology to study this problem and develop a useful educational research model.
2. Our Proposed Solution
We propose designing and programming a smart environmental monitoring robot called VEX AirGuard NM.
Using our VEX V5 competition robot, we will develop a mobile system that travels to different classroom locations and records environmental measurements.
The robot will carry sensors that measure:
Carbon dioxide (CO₂): Helps us investigate ventilation.
Temperature: Measures classroom air temperature.
Humidity: Helps us study moisture conditions.
Optional PM2.5 sensor: Measures fine airborne particles that may be associated with respiratory irritation and wildfire smoke.
Our goal is to create a computer-generated map of classroom environmental conditions.
Students will use Python programming to analyze the data and investigate how environmental conditions may change during the school day.
The project will not diagnose asthma or determine whether an individual student is at risk.
3. Research Question
Can a VEX V5 robot combined with Python computational modeling create an accurate map of classroom CO₂ levels more efficiently than traditional fixed-location measurement methods?
We will also investigate whether adding measurements of temperature, humidity, and fine particles can provide a more complete picture of indoor environmental conditions.
4. Our Hypothesis
We predict that a robot using a smart computer-guided measurement strategy will produce a more accurate classroom CO₂ map than a robot following a fixed route when both use the same number of measurement stops.
We also predict that computational modeling can help estimate how classroom CO₂ concentrations change with occupancy and ventilation.
5. How Our Robot Will Work
Our VEX V5 robot will move through a classroom using a programmed route.
At each measurement station, the robot will:
Stop at a designated location.
Allow the sensor reading to stabilize.
Record CO₂, temperature, and humidity.
Save the measurement location and time.
Move to the next station.
The collected information will be transferred to a computer.
Our Python program will use the information to create a colored map showing how CO₂ concentrations differ across the classroom.
6. Computational Science and Supercomputing
The computational model is the most important part of our project.
Our team will develop a Python program that represents a classroom as a grid.
The computer will use mathematical calculations to estimate CO₂ concentrations in locations that have not been measured directly.
Students will compare three measurement strategies:
Method 1: Fixed Route
The robot follows the same predetermined path.
Method 2: Grid Search
The robot visits measurement locations in a systematic pattern.
Method 3: Smart Computational Search
The computer analyzes information from previous measurements and selects the next location where additional data may improve the map.
Our smart algorithm will consider estimated measurement uncertainty and travel distance.
We will also develop a simplified mathematical model to predict changes in classroom CO₂ concentrations over time.
Students will run hundreds or thousands of computer simulations with different classroom layouts, occupancy assumptions, and ventilation conditions.
The simulations will allow us to compare the three methods under controlled conditions.
7. Experimental Research
We plan to conduct controlled classroom experiments using the physical VEX V5 robot.
Students will record the time, travel distance, and environmental measurements collected during each mission.
We will use a separate reference sensor or reference measurement stations to evaluate the accuracy of our computer-generated maps.
We will conduct repeated trials and compare the three strategies under similar classroom conditions.
Our measurements will include:
- Average CO₂ concentration in parts per million
- Temperature and relative humidity
- Optional PM2.5 concentration
- Number of measurement stops
- Total distance traveled
- Time required to complete each route
- Accuracy of the estimated classroom map
- Difference between predicted and measured CO₂ levels
We will use Python to create graphs and tables showing our experimental results.
8. Why Students With Asthma and Respiratory Conditions Matter
One of the strongest motivations for this project is our desire to support a school environment where all students can participate and learn comfortably.
Some students live with asthma or other respiratory conditions that make them particularly sensitive to certain environmental triggers.
Our robot will not identify which students have medical conditions, and we will not collect private student health information.
Instead, our project will focus on environmental measurements that may help school personnel understand general classroom conditions.
Because CO₂ is mainly useful for studying ventilation, we will not use CO₂ readings to claim that an asthma trigger is present or that a classroom is medically safe.
If we can include a PM2.5 sensor, we can also investigate fine airborne particles, an important environmental consideration when studying smoke and respiratory health.
Our goal is to use science responsibly while raising awareness about the importance of indoor air quality.
9. Expected Results
We expect that CO₂ concentrations may vary by location and change during the school day.
We also expect that a smart computational measurement strategy may improve our ability to estimate classroom CO₂ concentrations.
However, our experiments may reveal that better accuracy requires additional travel time.
We will investigate these differences and use mathematical evidence to determine which strategy performs best.
Our final conclusions will be based on collected data rather than assumptions.
10. Importance to New Mexico Schools
This project connects robotics and computer science to a real-world environmental problem.
It allows middle-school students to investigate how technology can be used to study indoor conditions that matter to school communities, including students who have asthma, allergies, and respiratory sensitivities.
The project may also inspire future investigations into environmental monitoring in libraries, offices, and healthcare buildings.
Our research is educational and will not replace professional ventilation assessments, medical guidance, or school safety procedures.
11. Our Final Goal
Our team wants to demonstrate that sixth-grade students can use engineering, mathematics, robotics, and computer science to investigate a meaningful problem in their community.
We will present our working VEX robot, Python simulation, classroom maps, mathematical analysis, and experimental results to the judges.
By combining physical robotics with computational modeling, we hope to discover more efficient ways to collect and interpret environmental measurements.
VEX AirGuard NM is more than a robot. It is a student-led computational science project that explores how technology can help us better understand the air in our classrooms.
Our Mission:
Better Data. Smarter Robots. Greater Awareness. Healthier Learning Environments.
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