Robotics Software Engineer

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.

Learn the essential element of robotics.

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

What to do if you have questions about your account or general questions about the program.

Get started by exploring the Udacity Workspace, optionally installing a Virtual Machine, and learning prep materials to help you succeed in this program.

Learn how to simulate your first robot in Gazebo and interact with its world using a Plugin.

Obtain an architectural overview of the Robot Operating System Framework and setup your own ROS environment in the Udacity Workspace.

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.

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++.

Introduction to the localization concept and the algorithms

Learn the Kalman Filter and Extended Kalman Filter Gaussian estimation algorithms.

Learn how to apply an EKF ROS package to a robot to estimate its pose.

Learn the Monte Carlo Localization algorithm which uses particle filters to estimate a robot's pose.

Learn how to code the Monte Carlo Localization algorithm in C++.

Introduction to the Mapping and SLAM concepts, as well as the algorithms.

Learn how to map an environment with the Occupancy Grid Mapping algorithm.

Learn how to simultaneously map an environment and localize a robot relative to the map with the Grid-based FastSLAM algorithm.

Learn how to simultaneously map an environment and localize a robot relative to the map with the GraphSLAM algorithm.

Learn what the lessons in Path Planning and Navigation will cover.

Learn a number of classic path planning approaches that can be applied to low-dimensional robotic systems.

Learn to code the BFS and A* algorithms in C++.

Learn about sample-based and probabilistic path planning and how they can improve on the classic approach.

Plan a path through a maze for an industrial Kuka Manipulator Arm, then watch your code run on real hardware!

Helpful instruction to help you get started with the project