2022 Course Syllabus

Learn more about the program structure.

Table of contents

1. Instructors
2. DJI Tello & Passport Curriculum
3. Week 1: Quadcopter Basics (7/11)
4. Week 2: Computer Vision & Machine Learning (ML) (7/18)
5. Week 3: Planning and Control (7/25)
6. Week 4: Final Project (8/1-8/5)

Instructors

Lead Instructors

1. Nathaniel Hanson, nathaniel.hanson@ll.mit.edu
2. Aryk Ledet, Aryk.Ledet@ll.mit.edu

Associate Instructors

1. Ifueko Igbinedion, ifueko@mit.edu
2. Matthew Boyd, mcboyd@bu.edu

Teaching Assistants

1. Matthew Schofield, mschof@mit.edu
2. Rumaisa Abdulhai, rumaisa@mit.edu

Please contact our staff via the official Discord server. If you don’t have access, please reach out to the lead instructors.

DJI Tello & Passport Curriculum

The UAV course staff have modified the Intel Aero Course content previously used for in person instruction for with the DJI Tello mini-drone. The drone has core robotic functionality such as an IMU, onboard camera, and Python SDK. There is also an active community of users providing support for the platform. While an economic replacement, it does not offer all the same sensory capabilities.

To manage remote learning, practicals here are called passports. Each student upon receiving a drone is issued a passport, which contains incremental exercises for development of robotic disciplines: controls, machine vision, etc. Instructors or teaching assistants sign off on exercises as they are completed.

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Week 1: Quadcopter Basics (7/11)

Goal: We will learn about drones and cutting edge UAV research, review Unix and Version Control using Git, and understand the architecture of drones that will culminate in our first challenge of the program.

• [07/11] Mon: BWSI Kickoff, Intro & Setup

• [07/12] Tue: Intro to Unix, Debugging & Git, PEP8 Standard

• [07/13] Wed: UAV Hardware, UAV Safety, Intro to Tello, Reintro to ROS

• [07/14] Thu: ROS Guide, Intro to Challenge 1

• [07/15] Fri: Localization & Reference Frames, Guest Speaker 1, Challenge 1

Week 2: Computer Vision & Machine Learning (ML) (7/18)

Goal: We will review Linear Algebra concepts, learn about tools that enable autonomous agents to understand their surroundings using RGB images, and grasp how convolutional neural networks work.

• [07/18] Mon: Intro to Computer Vision, Matrices & Numpy

• [07/19] Tue: OpenCV, Color Segmentation

• [07/20] Wed: Intro to Probability, Intro to Machine Learning Pt. 1

• [07/21] Thu: Intro to Machine Learning Pt. 2, Advanced Topics: Machine Learning in Computer Vision

• [07/22] Fri: Intro to Tags, Challenge 2

Week 3: Planning and Control (7/25)

Goal: We will understand how autonomous systems make decisions and execute tasks with precision.

• [07/25] Mon: Guest Speaker 2, Intro to Control Theory, Reference Frames & Transforms, Continuation of Challenge 2

• [07/26] Tue: Euler Angles & Quaternions, Equations of Motion, Estimators

• [07/27] Wed: Motion Planning, Feedback & PID Control

• [07/28] Thu: Simultaneous Localization & Mapping (SLAM) + Optical Flow, Tello Passport 3

• [07/29] Fri: Applications with ROS, Introduce Final Challlenge

Week 4: Final Project (8/1-8/5)

Goal: We will spend the whole week working on the Final Projects in teams.

Since a standardized UAV cage is not readily available in every student’s home, we encourage summer students to choose an open-ended project that encompasses the skills they have learned in the prep coursework and over the summer. In past years, some students built their own race course out of pool noodles and others applied more advanced machine learning topics to create a persona videography drone. If you need project ideas, don’t feel shy about asking the instructors!

The final challenge presentations will be on 08/06 and 08/07 (Saturday and Sunday).

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