Become a Robotics Software Engineer

Become a Robotics Software Engineer
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Date/Time
Date(s) - 01/01/2019 - 01/05/2020
12:00 am

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Build hands-on projects to acquire core robotics software engineering skills: ROS, Gazebo, Localization, Mapping, SLAM, Navigation, and Path Planning.

Why Take This Nanodegree Program?

In this program, you’ll learn core robotics skills necessary for success in the field: Localization, Mapping, Simultaneous Localization and Mapping (SLAM), Path Planning, and Navigation. You’ll implement these algorithms with C++, Robot Operating System (ROS), and the Gazebo simulator, and complete five hands-on projects to showcase your skills to hiring managers.

Advance your Career

This program was built in collaboration with robotics engineers to ensure you learn the skills necessary for success in the field. Demand for skilled robotics engineers is skyrocketing, but it’s important to understand and learn what it really takes to be a qualified robotics software engineer. This program prioritizes mastering job-ready skills with a hands-on approach.

What You Will Learn

Robotics Software Engineer

Begin your exploration into the world of robotics software engineering with a practical, system-focused approach to programming robots using the ROS framework and C++. In addition, learn and apply robotics software engineering algorithms such as localization, mapping, and navigation.

  • 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.

  • Gazebo World

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

  • 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++.

  • 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).

  • 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.

  • 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!

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