Robotics Software Engineer v5.0.0

Nanodegree key: nd209

Version: 5.0.0

Locale: en-us

Fuse computer vision, machine learning, mechanics, and hardware systems to build bots of the future!

Content

**Part 01 (FreePreview) :** Free Preview

Module 01: Free Preview Lessons
- Lesson 01: Gazebo Basics
  
  Learn how to simulate your first robot in Gazebo and interact with its world using a Plugin.
- Lesson 02: Introduction to ROS
  
  Obtain an architectural overview of the Robot Operating System Framework and setup your own ROS environment in the Udacity Workspace.

Part 02 : Introduction to Robotics

Learn the essential elements of robotics, meet your instructors, and get familiar with the tools that will help you succeed in this program.

Module 01: Introduction to Robotics
- Lesson 01: Welcome
  
  In this first lesson, you'll meet your instructors, learn about the structure of this program, and see the services available to you as a student.
- Lesson 02: What is a Robot?
  
  Learn the essential element of robotics.
- Lesson 03: Career Support Overview
  
  As you learn the skills you’ll need in order to work in the robotics industry, you’ll see Career Lessons and Projects that will help you improve your online professional profiles.
- Lesson 04: 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 05: 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
- Lesson 06: Getting Started
  
  Get started by exploring the Udacity Workspace, optionally installing a Virtual Machine, and learning prep materials to help you succeed in this program.

Part 03 : Gazebo World

Learn how to simulate your first robotic environment with Gazebo, the most common simulation engine used by Roboticists around the world.

Module 01: Gazebo World
- Lesson 01: Gazebo Basics
  
  Learn how to simulate your first robot in Gazebo and interact with its world using a Plugin.
- Lesson 02: Project: Build My World
  
  In this project, you will design a Gazebo World environment by including multiple models and use it as a base for all your upcoming projects.
  
  Project Description - Build My World
  
  Project Rubric - Build My World

Part 04 : ROS Essentials

Discover how ROS provides a flexible and unified software environment for developing robots in a modular and reusable manner. Learn how to manage existing ROS packages within a project, and how to write ROS Nodes of your own in C++.

Module 01: ROS Essentials
- Lesson 01: Introduction to ROS
  
  Obtain an architectural overview of the Robot Operating System Framework and setup your own ROS environment in the Udacity Workspace.
- Lesson 02: Packages & Catkin Workspaces
  
  Learn about ROS workspace structure, essential command line utilities, and how to manage software packages within a project. Harnessing these will be key to building shippable software using ROS.
- Lesson 03: Write ROS Nodes
  
  ROS Nodes are a key abstraction that allows a robot system to be built modularly. In this lesson, you'll learn how to write them using C++.
- Lesson 04: Project: Go Chase It!
  
  Design and build a mobile robot, and house it in your world. Then, program your robot with C++ nodes in ROS to chase white colored balls!
  
  Project Description - Go Chase It!
  
  Project Rubric - Go Chase It!
- Lesson 05: 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

Part 05 : Localization

Learn how Gaussian filters can be used to estimate noisy sensor readings, and how to estimate a robot’s position relative to a known map of the environment with Monte Carlo Localization (MCL).

Module 01: Localization
- Lesson 01: Introduction to Localization
  
  Introduction to the localization concept and the algorithms
- Lesson 02: Kalman Filters
  
  Learn the Kalman Filter and Extended Kalman Filter Gaussian estimation algorithms.
- Lesson 03: Lab: Kalman Filters
  
  Learn how to apply an EKF ROS package to a robot to estimate its pose.
- Lesson 04: Monte Carlo Localization
  
  Learn the Monte Carlo Localization algorithm which uses particle filters to estimate a robot's pose.
- Lesson 05: Build MCL in C++
  
  Learn how to code the Monte Carlo Localization algorithm in C++.
- Lesson 06: Where Am I?
  
  Use the Adaptive Monte Carlo Localization algorithm in ROS to localize your robot!
  
  Project Description - Where Am I
  
  Project Rubric - Where Am I

Part 06 : Mapping and SLAM

Learn how to create a Simultaneous Localization and Mapping (SLAM) implementation with ROS packages and C++. You’ll achieve this by combining mapping algorithms with what you learned in the localization lessons.

Module 01: Mapping and SLAM
- Lesson 01: Introduction to Mapping and SLAM
  
  Introduction to the Mapping and SLAM concepts, as well as the algorithms.
- Lesson 02: Occupancy Grid Mapping
  
  Learn how to map an environment with the Occupancy Grid Mapping algorithm.
- Lesson 03: Grid-based FastSLAM
  
  Learn how to simultaneously map an environment and localize a robot relative to the map with the Grid-based FastSLAM algorithm.
- Lesson 04: GraphSLAM
  
  Learn how to simultaneously map an environment and localize a robot relative to the map with the GraphSLAM algorithm.
- Lesson 05: Map My World
  
  Deploy RTAB-Map on your simulated robot to create 2D and 3D maps of your environment!
  
  Project Description - Map My World
  
  Project Rubric - Map My World
- Lesson 06: 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 07 : Path Planning and Navigation

Learn different Path Planning and Navigation algorithms. Then, combine SLAM and Navigation into a home service robot that can autonomously transport objects in your home!

Module 01: Path Planning and Navigation
- Lesson 01: Intro to Path Planning and Navigation
  
  Learn what the lessons in Path Planning and Navigation will cover.
  - Concept 01: Lesson Overview
  - Concept 02: Applications
- Lesson 02: Classic Path Planning
  
  Learn a number of classic path planning approaches that can be applied to low-dimensional robotic systems.
- Lesson 03: Lab: Path Planning
  
  Learn to code the BFS and A* algorithms in C++.
- Lesson 04: Sample-Based and Probabilistic Path Planning
  
  Learn about sample-based and probabilistic path planning and how they can improve on the classic approach.
- Lesson 05: Home Service Robot
  
  Program a home service robot that will autonomously map an environment and navigate to pickup and deliver objects!
  
  Project Description - Home Service Robot
  
  Project Rubric - Home Service Robot

**Part 08 (Elective) :** Optional KUKA Path Planning Project

Module 01: Optional Kuka Path Planning Project
- Lesson 01: Project Introduction
  
  Plan a path through a maze for an industrial Kuka Manipulator Arm, then watch your code run on real hardware!
- Lesson 02: Project Details
  
  Helpful instruction to help you get started with the project

**Part 09 (Elective) :** Autonomous Systems Interview

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!

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