## Graduating the Flying Car Nanodegree Term – Hacker Noon

### The Flying Car Nanodegree Term 1

The real deal.

Here’s a 50 feet view of the Complete Course:

The first term consists of seventeen lectures and four projects. Personally, I’m a fan of the Production quality. I think they are the MKBHDs of Education when it comes to the Video Production.

MATH! The Nanodegree is very very Math Heavy. If you’re a Math Person, you’d be much more comfortable on here.
The Programming too, is very challenging.

Udacity mentions the Nanodegree to be Advanced, I believe that is the toughest Nanodegree. The Code includes implementing Algorithms, and Math! and much learning of about physics. I really wish I had been a better student during school.

#### Term:

• Introduction: Introduction to Autonomous Flight and the Basics.
• Path planning: Path planning introduction and beyond: from A* on a grid to a real-world 3D planning.
• Control: Vehicle dynamics, control architecture(cascade PID controller) and full 3D control.
• Sensor Fusion and Estimation: Introduction to estimation, sensors(GPS, IMU, etc.) and extended/unscented Kalman filter.

### Backyard Flyer Project

We used Event Driven programming to create a basic Flight Controller.

This was very basic, I assumed the ND is a piece of cake- I was very wrong.

### Path Planning

We start from A* Search Algorithm in the 2D world to 3D motion planning using random sampling, graphs.

This project is a continuation of the Backyard Flyer project, we coded path planning through an urban environment in 3D!

### Control

MATH!

The Math in these lectures literally made gave me nightmares! This part had the toughtest lessons and Project.

The Key things taught were:
– PID Controller.
– Cascade PID Controller applied to a Drone.

This lecture was so difficult that they eventually relaxed the requirements and made us submit just the C++ implementation and removed the need for the Python submission.

#### Project:

The project consists of the implementation and tuning of a cascade PID controller for drone trajectory tracking.

### Sensor fusion and Estimation

This was divided into:

• Intro to Estimation.
• Sensors.
• Kalman Filters.
• GPS.

### Project:

We develop the estimation portion of the controller used in the CPP simulator.

There are various scenarios that we had to pass:
– Implement a better rate gyro attitude integration scheme
– The prediction step should include a correct calculation of the Rgb prime matrix, and a proper update of the state covariance.
– Implement the magnetometer update.
– Implement the GPS update.