Table of Contents
Introduction to Kalman Filters by Michel van Biezen
Introduction to GPS by Michel van Biezen
Control System Lectures by Brian Douglas
Forward and Inverse Kinematics by Coding Math
Artificial Intelligence for Robotics by Sebastian Thrun (Udacity)
SLAM tutorial by Prof. Claus Brenner
Visual Navigation for Flying Robots by Dr. Jürgen Sturm
Robotics - ColumbiaX - CSMM.103x - edX
- Overview
- Course Outline

Introduction to Kalman Filters by Michel van Biezen

playlist with 55 videos

Introduction to GPS by Michel van Biezen

playlist with 18 videos

Control System Lectures by Brian Douglas

PID Control: playlist with 2 videos

Forward and Inverse Kinematics by Coding Math

Demo with implementation in Javascript.

Lecture 1: video

Lecture 2: video

Lecture 3: video

Lecture 4: video

Artificial Intelligence for Robotics by Sebastian Thrun (Udacity)

A course created by Sebastian Thrun on the on-line educational platform Udacity. It covers the basic techniques for self-driving robots/cars. The course material is presented in a series of videos accessible via the Udacity platform. This course is very motivational. The exercises sometimes vary highly in difficulty though. The solution code for the exercises is always given. There’s also an on-line forum where students can ask questions. This is a good place to start.

Link to the class Wiki

Lesson 1: The problem of localization

Robot sensing and movement. Probabilistic techniques to deal with inaccuracies. Probability theory basics like Bayes theorem and law of total expectation. Finding position in 2D world.

Lesson notes - PDF

Lesson 2: Kalman Filters

Powerful localization method. Matrix operations to predict position and velocity of robot with linear motion. Conenctions between Kalman filter and Gaussian function and covariance.

Lesson notes - PDF

Lesson 3: Particle Filters

Alternative technique to Kalman Filter. Different possible robot positions are generated at random and evaluated according how close these are to the observed measurements. Particles are then reselected with probability. This creates a cluster around robot, reflecting its probable position.

Lesson notes - PDF

Lesson 4: Motion Planning

Planning robot motion. Path planning strategies A* search and Dynamic Programming.

Lesson notes - PDF

Lesson 5: PID Control

Path smoothing. PID controller to make robot follow a path. Smooth corrections when robot deviates from path.

Lesson notes - PDF

Lesson 6: GraphSLAM

Review of previous lessons. Method for simultaneous localization and mapping (SLAM). This method helps solving the problem of a robot in a world whose features are unknown.

Lesson notes - PDF

SLAM tutorial by Prof. Claus Brenner

A tutorial on Youtube on Simultaneous Location And Mapping by Prof. Claus Brenner. Starter code in Python can be downloaded using a link below the videos. No solution code is given. Only the expected results of running the solution code are given. This is a very nice tutorial to follow after “Artificial Intelligence for Robotics” by Sebastian Thrun.

Complete playlist:

playlist

Individual lectures:

Lecture 1: Introduction and History of Mobile Robotics

video

Lecture 2: Linear Algebra, 2D/3D geometry and Sensors

video

Lecture 3: Probabilistic Models and State Estimation

video

Lecture 4: Robot Control

video

Robotics - ColumbiaX - CSMM.103x - edX

Overview

Programming in ROS (Robot Operating System) with Python. Focus on Robot arms.

link

Course Outline

Week 1: Introduction to Robotics, Robotics and AI, Introduction to ROS, Project 1 released
Week 2: 2D Transforms, Homogenous Coordinates, 3D Transforms, Thinking about Transforms, Transform Inverse, Rotation Representations, Transforms in ROS, the TF Library, Project 2 released
Week 3: Robot Arms Introduction, Kinematic Chains, Forward Kinematics: URDF, Forward Kinematics: Analytical Methods, DH Parameters, Forward Kinematics:DH Examples, Project 3 released
Week 4: Analytical IK, Robot Examples, Robot Workspaces and IK Solutions, Homework 1 released
Week 5: Differential Kinematics: Jacobian Definition and Analytical Computation, Singularities, Full Kinematics: Robot Examples, Homework 2 released
Week 6: Study week
Week 7: Numerical Jacobian Computation, Cartesian and Null Space Control, Porject 4 released
Week 8: Motion Planning: Configuration Space vs. Task Space, Stochastic Motion Planning, Project 5 released
Week 9: Mobile Robots Introduction, Mobile Robots Kinematics: Differential Drive, Other Kinematics, Path Planning for Mobile Robots
Week 10: Course Recap, Things We Have Not Covered, Robotics and AI Revisited

Total Robotics

A collection of course materials and lectures about robotics.

Table of Contents

Introduction to Kalman Filters by Michel van Biezen

Introduction to GPS by Michel van Biezen

Control System Lectures by Brian Douglas

Forward and Inverse Kinematics by Coding Math

Artificial Intelligence for Robotics by Sebastian Thrun (Udacity)

Lesson 1: The problem of localization

Lesson 2: Kalman Filters

Lesson 3: Particle Filters

Lesson 4: Motion Planning

Lesson 5: PID Control

Lesson 6: GraphSLAM

SLAM tutorial by Prof. Claus Brenner

Unit A: Getting started with a real robot

Unit B: Using sensor data to improve the robot’s state

Unit C: Filtering

Unit D: Kalman Filter

Unit E: Particle Filter

Unit F: Simultaneous Localization and Mapping

Unit G: Particle Filter SLAM - FastSLAM

Unit PP: Basics of Path Planning

Visual Navigation for Flying Robots by Dr. Jürgen Sturm

Lecture 1: Introduction and History of Mobile Robotics

Lecture 2: Linear Algebra, 2D/3D geometry and Sensors

Lecture 3: Probabilistic Models and State Estimation

Lecture 4: Robot Control

Robotics - ColumbiaX - CSMM.103x - edX

Overview

Course Outline