Robotics Software Engineer Specialization

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About This Course

The Robotics Software Engineer Nanodegree program focuses on teaching the core robotics skills needed for a successful robotics software engineering career. The program focuses on Localization, Mapping, SLAM, Path Planning, and Navigation. These are taught using C++ and the Robot Operating System (ROS) framework.


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3 months program

Certificate

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You can share your Certificates in the Certifications section of your LinkedIn profile, on your printed resume, or in other documents.

Learning Objectives

The Robotics Software Engineer Nanodegree program is comprised of content and curriculum to support five (5) projects. We estimate that students can complete the program in four (4) months, working 10 hours per week.

Requirements

  • To succeed in this Nanodegree program, you should have experience with the following:
  • Advanced knowledge in any object-oriented programming language, preferably C++
  • Intermediate Probability
  • Intermediate Calculus
  • Intermediate Linear Algebra
  • Basic Linux Command Lines

Curriculum

761 Lessons

Welcome

Learn how to simulate your first robotic environment with Gazebo, the most common simulation engine used by Roboticists around the world.
Meet Your Instructors00:2:10Preview
Projects You Will Build00:1:39Preview
Connecting with Your Community
Access the Career Portal
How Do I Find Time for My Nanodegree?00:00:00

What is a Robot

Search and Sample Return

Career Support Overview

Get Help from Peers and Mentors

Explores – Biologically Inspired Robots

Questions on Robotics Careers

Intro to Kinematics

Forward and Inverse Kinematics

Explores – Human Robot Interaction Robot Ethics

Product Pitch

Perception Overview

Introduction to 3D Perception

Calibration, Filtering, and Segmentation

Clustering for Segmentation

Object Recognition

3D Perception Project

Introduction to 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).

Kalman Filters

Lab Kalman Filters

Monte Carlo Localization

Build MCL in C++

Project Where Am I

Introduction to 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.

Occupancy Grid Mapping

Grid-based FastSLAM

GraphSLAM

Project Map My World Robot

Intro to 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!

Classic Path Planning

Lab Path Planning

Sample-Based and Probabilistic Path Planning

Research in Navigation

Project Home Service Robot

Completing the Program

Project Introduction

Project Details

Autonomous Systems Interview Practice

Convolutional Neural Networks

Explores – Soft Robotics

Explores – Robot Grasping

Introduction to Controls

Quadrotor Control using PID

Explores Swarm Robotics

Networking in Robotics

Intro to Neural Networks

TensorFlow for Deep Learning

Deep Neural Networks

Fully Convolutional Networks

Lab Semantic Segmentation

Project Follow Me

Term 1 Outro

Introduction to C++ for Robotics

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++.

Introduction to Term 2

The Jetson TX2

Interacting with Robotics Hardware

Lab Hardware Hello World

Robotics Sensor Options

Inference Development

Inference Applications in Robotics

Project Robotic Inference

Intro to RL for Robotics

RL Basics

Q-Learning Lab

Deep RL

DQN Lab

Deep RL Manipulator

Project Deep RL Arm Manipulation——–

Strengthen Your Online Presence Using LinkedIn —

GitHub

Optimize Your GitHub Profile

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