Flying Car and Autonomous Flight Engineer v5.0.0

Nanodegree key: nd787

Version: 5.0.0

Locale: en-us

Master autonomous flight software engineering skills as you learn about drone robotics, develop sophisticated flying car systems, and write real code for real aircraft.

Content

Part 01 : Introduction to Autonomous Flight

In this course, you will get an introduction to flight history, challenges, and vehicles. You will learn about our quadrotor test platform, work in our custom simulator, and build your first project—getting a quadrotor to take-off and fly around a backyard!

Module 01: Introduction to Autonomous Flight
- Lesson 01: Welcome!
  
  In this lesson you'll meet your instructors and go over some of the logistical details of this Nanodegree program.
- Lesson 02: Autonomous Flight
  
  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.
- Lesson 03: Backyard Flyer
  
  In this lesson you'll write the "Hello, world!" of drone programming as you write event-driven code that causes a quadrotor to take off, fly in a square, and land.
  
  Project Description - Backyard Flyer
  
  Project Rubric - Backyard Flyer
- Lesson 04: Drone Integration
  
  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.
- Lesson 05: Nanodegree Career Services
  
  The Careers team at Udacity is here to help you move forward in your career - whether it's finding a new job, exploring a new career path, or applying new skills to your current job.
  - Concept 01: Access the Career Portal
- Lesson 06: Knowledge, Community, and Careers
  
  You are starting a challenging but rewarding journey! Take 5 minutes to read how to get help with projects and content.
- Lesson 07: Get Help with Your Account
  
  What to do if you have questions about your account or general questions about the program.
  - Concept 01: FAQ
  - Concept 02: Support

Part 02 : Planning

Flying robots must traverse complex, dynamic environments. Wind, obstacles, unreliable sensor data, and other randomness all present significant challenges. In this course, you will learn the fundamentals of aerial path planning. You will begin with 2D problems, optimize your solutions using waypoints, and then scale your solutions to three dimensions. You will apply these skills in your second project—autonomously navigating your drone through a dense urban environment.

Module 01: 3D Motion Planning
- Lesson 01: Planning as Search
  
  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!
- Lesson 02: Flying Car Representation
  
  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.
- Lesson 03: From Grids to Graphs
  
  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.
- Lesson 04: Moving into 3D
  
  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.
- Lesson 05: Real World Planning
  
  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.
- Lesson 06: Project: 3D Motion Planning
  
  In this project, you'll get a chance to apply what you've learned about 3D motion planning from the last several lessons to plan and execute a mission in a complex urban environment!
  
  Project Description - 3D Motion Planning
  
  Project Rubric - 3D Motion Planning

Part 03 : Controls

In the previous course, we implemented 3D path planning but assumed a solution for actually following paths. In reality, moving a flying vehicle requires determining appropriate low-level motor controls. In this course, you will build a nonlinear cascaded controller and incorporate it into your software in the project.

Module 01: Controls
- Lesson 01: Vehicle Dynamics
  
  Learn how flying vehicles move in one and two dimensions by understanding how propellers create forces and moments which cause accelerations and rotations.
- Lesson 02: Introduction to Vehicle Control
  
  Learn how to control a drone moving in one dimension using Proportional Integral Derivative (PID) Control.
- Lesson 03: Control Architecture
  
  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.
- Lesson 04: Full 3D Control
  
  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.
- Lesson 05: Project: Building a Controller
  
  In this project you'll implement a controller for a quadrotor in C++.
  
  Project Description - Control of a 3D Quadrotor
  
  Project Rubric - Control of a 3D Quadrotor
- Lesson 06: Drone Integration
  
  Walkthrough the steps you need to take to get a version of your controls project on a crazyflie!

Part 04 : Estimation

In this course, we will finish peeling back the layers of your autonomous flight solution. Instead of assuming perfect sensor readings, you will utilize sensor fusion and filtering. You will design an Extended Kalman Filter (EKF) to estimate attitude and position from IMU and GPS data of a flying robot.

Module 01: Estimation
- Lesson 01: Introduction to Estimation
  
  Review basic probability and learn three approaches to state estimation for a stationary vehicle.
- Lesson 02: Introduction to Sensors
  
  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.
- Lesson 03: Extended Kalman Filters
  
  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.
- Lesson 04: The 3D EKF and UKF
  
  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.
- Lesson 05: Project: Estimation
  
  In this project you'll implement an estimator to track the position and attitude of a quadrotor moving in three dimensions.
  
  Project Description - Building an Estimator
  
  Project Rubric - Building an Estimator
  - Concept 01: Introduction
- Lesson 06: GPS Denied Navigation
  
  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.

Part 05 : Career Support

GitHub and LinkedIn are both widely used by tech recruiters to find and evaluate job candidates. Get personalized feedback on how to improve yours! You may elect to opt-out of these projects.

Module 01: Career Support
- Lesson 01: Optimize Your GitHub Profile
  
  Other professionals are collaborating on GitHub and growing their network. Submit your profile to ensure your profile is on par with leaders in your field.
  
  Project Description - Optimize Your GitHub Profile
  
  Project Rubric - Optimize Your GitHub Profile
- Lesson 02: Take 30 Min to Improve your LinkedIn
  
  Find your next job or connect with industry peers on LinkedIn. Ensure your profile attracts relevant leads that will grow your professional network.
  
  Project Description - Improve Your LinkedIn Profile
  
  Project Rubric - Improve Your LinkedIn Profile

Part 06 : Congratulations!

Module 01: Congratulations
- Lesson 01: Congratulations!
  
  Congratulations! You've completed all the requirements for this Nanodegree Program. Pat yourself on the back and share your accomplishment with the world!

Part 07 : [Optional] Fixed Wing

While quadrotors are the ideal test platform for aerial robotics, flying cars and other long-range aircrafts leverage the aerodynamic efficiencies of fixed-wing flight. In this course, you will learn how to adapt the concepts you’ve learned so far and successfully fly a fixed-wing aircraft in simulation.

Module 01: [Optional] Fixed Wing
- Lesson 01: Introduction to Fixed-Wing Flight
  
  This lesson provides a brief introduction to Fixed Wing Vehicles, flying cars, and the components of typical fixed-wing aircraft.
- Lesson 02: Lift and Drag
  
  Build mathematical models for lift and drag, the aerodynamic forces that make fixed wing flight possible (and difficult).
- Lesson 03: Longitudinal Model
  
  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.
- Lesson 04: Lateral-Directional Model
  
  Understand the lateral-directional dynamics of fixed wing vehicles by looking at aircraft from above and behind.
- Lesson 05: Fixed-Wing Autopilot
  
  Apply the concepts of PID control by implementing an autopilot for fixed wing flight.
- Lesson 06: Optional Project: Fixed-Wing Control
  
  In this optional project you will control a simulated fixed-wing aircraft by implementing and tuning your own autopilot in Python.
  - Concept 01: Fixed Wing Autopilot
  - Concept 02: Hybrid VTOL

**Part 08 (Elective) :** Autonomous Systems Interview

Module 01: Autonomous Systems Interview
- Lesson 01: Autonomous Systems Interview Practice
  
  Start off with some tips on interviewing for an autonomous systems role, then watch how candidates approach their interview questions. Finish off by practicing some questions of your own!
  
  Project Description - Autonomous Systems Interview Practice Project
  
  Project Rubric - Autonomous Systems Interview Practice Project