Flying Car and Autonomous Flight Engineer

Welcome! We're so glad you're here. Join us in learning a bit more about what to expect and ways to succeed.

You are starting a challenging but rewarding journey! Take 5 minutes to read how to get help with projects and content.

In this lesson you'll meet your instructors and go over some of the logistical details of this Nanodegree program.

In this lesson you'll get a high level overview of the concepts underlying autonomous flight and the physical components from which flying vehicles are made.

Walkthrough the steps you need to take to get your code running on an actual drone! We'll show you the steps for the "Intel Aero", but a lot of what you'll learn applies to other drones as well.

Solving the planning problem really comes down performing search through a state space to find a path from a start state to a goal state and here you'll get a chance to do just that!

Your vehicle has a physical size and orientation in the world and here you'll learn how to think about position and orientation as part of your planning solution.

Graphs are really just a way of describing how your search space is connected. Here you'll learn about the tradeoffs between grids and graphs and each can be used in your planning representation.

Here you'll make the leap from two dimensions to three dimensions and discover how you can use different representations of your search space to optimize your planning solution.

In this lesson, you'll dive deep into some advanced concepts that are crucial to motion planning in the real world, where a consideration for physics and preparedness for the unexpected are crucial.

Learn how flying vehicles move in one and two dimensions by understanding how propellers create forces and moments which cause accelerations and rotations.

Learn how to control a drone moving in one dimension using Proportional Integral Derivative (PID) Control.

The controls problem becomes more difficult in two dimensions. Learn how to use a cascaded PID control architecture to control a flying vehicle that moves in two dimensions.

In this lesson you'll take everything you've learned so far about vehicle dynamics and control and put it together to control a quadrotor that moves in three dimensions.

Walkthrough the steps you need to take to get a version of your controls project on a crazyflie!

Review basic probability and learn three approaches to state estimation for a stationary vehicle.

In this lesson you'll learn about the sensors a drone uses to localize itself in the world. You'll implement sensor models, analyze sources of error, and perform calibration of various sensors.

In this lesson you'll learn how to estimate the state of a drone that's actually moving! You'll implement a Kalman Filter for a 1D drone and an Extended Kalman Filter for a non-linear 2D drone.

Take what you learned in the previous lesson and generalize to three dimensions. After learning about the 3D EKF you'll also learn another estimation algorithm called the Unscented Kalman Filter.

How do you estimate vehicle state when you don't have GPS? In this lesson you'll learn about optical flow and particle filters as two approaches to solving this problem.

Congratulations! You've completed all the requirements for this Nanodegree Program. Pat yourself on the back and share your accomplishment with the world!

This lesson provides a brief introduction to Fixed Wing Vehicles, flying cars, and the components of typical fixed-wing aircraft.

Build mathematical models for lift and drag, the aerodynamic forces that make fixed wing flight possible (and difficult).

Analyze both non-linear and linear models of a fixed-wing aircraft's motion in the x-z plane and use linear algebra to identify two oscillatory "modes" of motion.

Understand the lateral-directional dynamics of fixed wing vehicles by looking at aircraft from above and behind.

Apply the concepts of PID control by implementing an autopilot for fixed wing flight.

In this optional project you will control a simulated fixed-wing aircraft by implementing and tuning your own autopilot in Python.