GSOC 2017

Our 2017 Projects  Under the Python Software foundation:

Radiation is the transmission of energy in the form of waves or sub-atomic particles.In space missions the major concern is the particle radiation.Energetic particle radiation passing through human body could damage the cells or DNA causing an increased risk for cancer. Especially when out of Earth’s magnetic field protection, astronaut’s are exposed to ionizing radiation with doses in the range from 50 to 2,000 mSv(milli Sievert). The evidence of cancer risk from ionizing radiation is extensive for radiation doses that are above about 50 mSv. Hence it is important to forecast the radiation events and train crew members to tackle the issue of radiation effectively in simulated environments(Mars city project).

The aim of the project is to build a reliable system(Tango server) to :

1) Alert crew about the incoming SEP event if the radiation level is above the SWPC set threshold.

2) Issue the “all – clear” signal once the event has passed Mars.

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When astronauts travels to Mars, or even other planets, they will be exposed to a number of hazards e.g. radiation, microbes in the spacecraft, planetary surface toxic dust. This project mainly revolves around configuring biometric signal sensors.

The project has an initial phase of surveying the commercial sensors available, and selecting one, or many, after extensive research. The sensors are selected such that they read the biometrics like Heart Rate, Accelerometer data, Respiration rate, body temperature, Pulsioximetry data,Respiratory Volume, ECG, etc.

The second phase involves developing the Device Server itself in Tango, for these selected sensors. This project is part of the current studies on the simulation of Astronaut’s Health Monitor Systems.

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Implementing a vision based terrain traversability estimator for planetary rovers and astronauts to eliminate heavy hardware used in current techniques. Some of the rovers predict the slip from Wheel odometry but it would be more efficient to be able to predict it in advance if a terrain might be slippery, so as to make more intelligent path planning decisions and to reduce the hardware needed. Classified view of the terrain in front based on their traversability proves to be useful for the rover to take an optimum path and for an astronaut being a helping hand to suggest safest path that could be taken.

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