We have structured our curriculum in terms of “modules” where each module is supported by several types of materials to reinforce it. All modules have slides.

In the “Extra Materials” column below:
are links to notes in the duckiebook
are links to exercises in the duckiebook
are links to Jupyter notebooks in the duckiebook
are links to demos to be run on the Duckiebot
are links to additional videos that have been created

Topic

Lecture Slides

Lecture Recordings

Extra Materials

Introductory Materials

Introduction – Duckietown


Autonomous Vehicles
From TTIC 2017 (Matt Walter)
Introduction – Modern Robotics Systems

Modern Robotic Systems
From TTIC 2017 (Matt Walter)
Introduction – Architectures

System architecture basics
Autonomy architectures
From MIT 2016 (Misha Novitzky)
Introduction – Representations Representations
Tools – Networking
Tools – Version Control Git and Github
Tools – Middlewares ROS installation and reference
Taking and verifying a log
From MIT 2016 (Shih-Yuan Liu)

Modeling, Kinematics, and Dynamics

Modeling Duckiebot modeling
From TTIC 2017 (Matt Walter)
Calibration – Odometry Wheel calibration
Signal Processing Modern signal processing
Basics
Basic image operations
Instagram filters
OpenCV basics
From TTIC 2017 (Matt Walter)
Camera Models Augmented Reality
From TTIC 2017 (Matt Walter)
Feature Extraction SIFT
Image Filtering Image FilteringFrom TTIC 2017 (Matt Walter)
Line Detection From TTIC 2017 (Matt Walter)
Place Recognition From TTIC 2017 (Matt Walter)
RANSAC
Camera Calibration Camera calibration and validation

Estimation

Filtering Probability basics
Lane Filtering – Particle Filter
Lane Following
From TTIC 2017 (Matt Walter)
From MIT 2016 (John Leonard)
From MIT 2016 (Liam Paull)
Kalman Filter Lane Filtering – Extended Kalman Filtering
SLAM From TTIC 2017 (Matt Walter)

Planning and Control

Control

Make Way for Duckiebots
Lane Following
Motion Planning

Indefinite Navigation
Testing, Validation, Verification
Who watches the watchmen?

Multi-Vehicle

Multi-Vehicle Coordination
Fleet-level Planning Fleet planning
Introduction to Autonomous Mobility on Demand

Machine Learning

Machine Learning and Deep Learning – Introduction How to install PyTorch on the Duckiebot
How to install Caffe and Tensorflow on the Duckiebot
How to use the Neural Compute Stick
Models vs. Data
End-to-End Imitation Learning Lane control with supervised learning
Reinforcement Learning for Autonomous Vehicles

Human-Machine Interaction and Safety

Shared Control
Introduction to Safety
Advanced Safety and Formal Methods

Advanced Perception

Object Detection
Estimation from Motion Blur
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