Romanian high school students Cosmin Dumitru and Victor Arseniu has shown off a compact stacked-board design for a tiny satellite the size of a soda can — and suitable for monitoring atmospheric pollution at altitude as a bonus.
"A CanSat is a simulation of a real satellite, integrated within the volume and shape of a soft drink can," Dumitru explains. "The challenge for the students is to fit all the major subsystems found in a satellite, such as power, sensors, and a communication system, into this minimal volume. The CanSat is then launched to an altitude of a few hundred meters by a rocket or dropped from a platform or captive balloon and its mission begins: to carry out a scientific experiment and achieve a safe landing."
The TeamAir CanSat, as Dumitru and colleagues' creation is named, is a multi-board machine designed to confirm to the CanSat dimensions while packing sensors for monitoring temperature, pressure, ammonia, carbon dioxide, and air quality — the idea being that it could take readings at different altitudes on its descent to find out if pollution levels change depending on where in the atmosphere you are.
The CanSat uses a five-board layout: a power board with lithium-polymer battery and a power management system based on a Microchip ATtiny212; a microcontroller board, housing a Raspberry Pi Pico RP2040-based development board and an SD card for storage; a communications model with u-blox NEO-6M GPS module and Neoway M590E GSM cellular modem; a secondary storage board based on EEPROM technology, with a buzzer to more easily locate the hardware once it's landed; and the sensor board with MQ-135, MQ-137, MG811, DHT-11, and GMP180 sensors plus an MP6050 accelerometer and gyroscope.
TeamAir's mission, however, did not go smoothly: "Using the collected data, we would have studied the change in pollutants as the altitude increases," Dumitru explains. "Although due to some hardware problems unknown to this day most of the sensors partially or completely failed right during flight, but we managed to successfully correlate our data set with other existing ones which were relevant."