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== Sequoia GNC Onboarding ==
== Sequoia GNC Onboarding ==
No matter what background you have, you can contribute to Sequoia GNC! You can learn about the intersection between physics, CS, and engineering and especially control theory. Here's a few aspects of our system we currently need someone to work on:
No matter what background you have, you can contribute to Sequoia GNC! You can learn about the intersection between physics, CS, and engineering that is control theory. Here's a few aspects of our system we currently need someone to work on:
{| class="wikitable"
{| class="wikitable"
|+ Chose a Project!
|+ Choose a Project!
|-
|-
! Project || Background || Skills/Knowledge Involved
! Project || Background || Skills/Knowledge Involved
|}
|}
In addition to these projects that will allow you to jump right in to helping produce our GNC algorithms, there are a few practice onboarding projects we can guide you through where you can code your own simplified GNC subsystem. These are cool projects where you implement your own baby version of one of our core GNC algorithms to get you up to speed quickly. For example, a Kalman filter is an algorithm that takes in a bunch of noisy data from different sensors and uses knowledge of the dynamics of the system to predict the current state of the sattelite. It was invented in the early 60s to navigate people to the moon. One of the projects is coding a Kalman filter to predict the state simple dynamical systems (for example, balancing a broom on your hand by moving your hand back and forth to stabilize it). In addition to the Kalman filtering side of this, there is also a control side where you determine how much you should move your hand to keep the broom stable. Depending on how many people are interested, background, ect. We will set up either individuals or teams to write codes to tackle this GNC problem. Afterwards, you can jump into writing the equivalent code for our satellite. We will guide you through every step of the project, giving you all guidance and background materials necessary. We know this may seem complicated or daunting, but none of us started out knowing how to do this.
'''Or learn by writing your own mini GNC algorithm''' In addition to the above projects that will allow you to jump right in to helping produce our GNC algorithms, there are a few practice onboarding projects we can guide you through where you can code your own simplified GNC subsystem. These are cool projects where you implement your own baby version of one of our core GNC algorithms to get you up to speed quickly. For example, a Kalman filter is an algorithm that takes in a bunch of noisy data from different sensors and uses knowledge of the dynamics of the system to predict the current state of the sattelite. It was invented in the early 60s to navigate people to the moon. One of the projects is coding a Kalman filter to predict the state simple dynamical systems (for example, balancing a broom on your hand by moving your hand back and forth to stabilize it). In addition to the Kalman filtering side of this, there is also a control side where you determine how much you should move your hand to keep the broom stable. Depending on how many people are interested, background, ect. We will set up either individuals or teams to write codes to tackle this GNC problem. Afterwards, you can jump into writing the equivalent code for our satellite. We will guide you through every step of the project, giving you all guidance and background materials necessary. We know this may seem complicated or daunting, but none of us started out knowing how to do this.
If you're interested in working on the GNC components of the satellite, you can email the leads Alec Lessing (aml2023@stanford.edu) or Rodrigo Castellon (rjcaste@stanford.edu)!
If you're interested in working on the GNC components of the satellite, you can email the leads Alec Lessing (aml2023@stanford.edu) or Rodrigo Castellon (rjcaste@stanford.edu)!
[[File:SequoiaGNCArchitectureOverview2.png]]
[[File:SequoiaGNCArchitectureOverview2.png]]
'''Kalman Filter''' is an algorithm that takes in a bunch of noisy data from different sensors and uses knowledge of the dynamics of the system to predict the current state of the sattelite. It was invented in the early 60s to navigate people to the moon.
'''TortoiseSat''' is an experimental algorithm published by a Stanford lab a few months ago. It allows us to control our satellite using only magnet-torquers (small controlled current loops that create torque by interacting with Earth's magnetic field). The difficulty with this is that with magnet-torquers you can only generate a torque in two directions and not all three, which is why we need the experimental optimization algorithm to do it. We hope to demonstrate this algorithm on orbit for the first time with our satellite.