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Electrical Engineering
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Jan 9, 2024
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AER1217: AUTONOMY OF UAS
Centre for Aerial Robotics Research and Education
Winter/Spring 2022
AER1217 - Autonomy of UAS
Course Information
CARRE
©
Spring 2022
▶
Coordinator
Professor H. H-T. Liu
Institute for Aerospace Studies (UTIAS)
Room 185, Tel: (416) 667-7928, Fax: (416) 667-7799
Email:
hugh.liu@utoronto.ca
▶
Teaching Assistant
Mr. Wenda Zhao,
wenda.zhao@mail.utoronto.ca
Ms. Shangyi Xiong,
s.xiong@mail.utoronto.ca
2
AER1217 - Autonomy of UAS
Course Information
CARRE
©
Spring 2022
Curriculum
This graduate course is offered as part of CARRE program in
UAVs. This course is the second part of CARRE core courses
▶
prerequisite: AER1216: Fundamentals of UAS, unless
approved by the instructor;
▶
In this course, the focus is placed on the development of
unmanned aerial systems (UAS), with the theme of autonomy
in navigation and control;
▶
This is a group project oriented course; (details later)
▶
live tutorials are designed to help students learn and develop
skills step-by-step to prepare for the project;
▶
online/in-person lectures are integrated to provide relevant
knowledge base, with extensive coverage to broaden students’
horizons;
▶
For the Spring 2022 term, the course project is custom built
with expectation of computer simulations and laboratory
experiments (if possible).
3
AER1217 - Autonomy of UAS
Course Information
CARRE
©
Spring 2022
▶
Course Materials
▶
Course Presentation Handouts
(available through U of T portal)
▶
Lectures, Labs and Tutorials, Project presentations and
demonstration
▶
Mondays, 9h00-12h00 @online/in-person - time may be flexible
▶
Grading
▶
Laboratory (50%)
▶
Project (50%)
4
AER1217 - Autonomy of UAS
Course Information
CARRE
©
Spring 2022
Grading
▶
Computer Simulation Based Laboratory (50%)
▶
Lab 1 Demonstration (5%)
▶
Lab 2 Demonstration (5%) and Report (10%)
▶
Lab 3 Demonstration (5%) and Report (10%)
▶
Lab 4 Demonstration (5%) and Report (10%)
▶
Autonomous UAS Project (50%)
▶
Project Simulation/Experiments (15%)
▶
Project Presentation (10%)
▶
Project Report (25%)
5
AER1217 - Autonomy of UAS
Course Information
CARRE
©
Spring 2022
Agenda (2022S) I
1.
Information (Liu & TA) Jan.10
2.
Tutorial 1: Introduction to ROS and Python (TA) Jan.17
3.
Lab 1: ARdrone Simulator (demonstration) Jan.24
4.
Lecture 1: Quad-rotor dynamics and control (Schoellig):
Review (AER1216 Lecture 09) Jan.31
5.
Lab 2: Quadrotor Simulation and Position Control Design
(demonstration) Feb.07
6.
Lecture 2: Instrumentation and sensor payloads for UAVs
(Armenakis) Feb.14
Family Day (Feb.21) - university closed
7.
Lecture 3: Navigation for UAVs (Kelly) Feb.28
8.
Lecture 4: Visual Navigation for UAVs (Kelly) Mar.07
9.
Lab 3: Georeferencing Using UAV Payload Data
(demonstration) Mar.14
6
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AER1217 - Autonomy of UAS
Course Information
CARRE
©
Spring 2022
Agenda (2022S) II
10.
Tutorial 2: Visual Processing and OpenCV (TA) Mar.21
11.
Lab 4: Autonomous Drone Localization (demonstration)
Mar.28
12.
Lecture 5: Path Planning for UAVs (Waslander) Apr.04
13.
Project Apr.11
14.
Project Experiments Apr.18
15.
Project Experiments Apr.25
16.
Final Report Due Apr.30
7
AER1217 - Autonomy of UAS
Course Information
CARRE
©
Spring 2022
anyone graduating?
if anyone in class is expected to obtain the degree in June
convocation, please notify the instructor immediately!
8
AER1217 - Autonomy of UAS
Course Information
CARRE
©
Spring 2022
Timetable (2022S)
Date
Lecture Time
Lab Handout
Lab Time
Report Deadline
Jan. 10
Information
Jan. 14
Lab 1
Jan. 17
Tutorial 1
Jan. 24
Lab 1 Demo
Jan. 31
Lecture 1
Lab 2
Feb. 07
Lab 2 Demo
Feb. 14
Lecture 2
Lab 2 Report
Feb. 21
(no class)
Feb. 28
Lecture 3
Mar. 07
Lecture 4
Lab 3
Mar. 14
Lab 3 Demo
Mar. 21
Tutorial 2
Lab 4
Lab 3 Report
Mar. 28
Lab 4 Demo
Apr. 4
Lecture 5
Lab 4 Report
Apr. 11
Project
Apr. 18
Project Experiments
Apr. 25
Project Experiments
Apr. 30
Project Report
Table 1:
Timetable.
9
AER1217 - Autonomy of UAS
Course Information
CARRE
©
Spring 2022
Lab 1: ARdrone Simulator Setup
This lab requires the installation of ROS. It is set up to work with
Gazebo, which simulates the Parrot AR.Drone UAV in a controlled
and measured environment. You should have preliminary
knowledge of ROS, and Python programming (Tutorial 1). The lab
asks for a simulation demonstration. If the student runs into
problems of installing the ROS, it is advised that the students
defer the course to Spring 2022, or withdraw.
10
AER1217 - Autonomy of UAS
Course Information
CARRE
©
Spring 2022
Lab 2: Quadrotor Simulation and Position Control Design
This lab requires the design of a quadrotor position controller, and
implementation in ROS. It is set up to work with Gazebo, which
simulates the Parrot AR.Drone UAV in a controlled and measured
environment.
1.
Design and implementation of the position controller,
2.
Implementation of a ROS node that publishes control
commands and subscribes to quadrotor actual and desired
positions, and
3.
Implementation of a ROS node that publishes a time based
trajectory.
11
AER1217 - Autonomy of UAS
Course Information
CARRE
©
Spring 2022
Lab 3: Georeferencing Using UAV Payload Data
In this lab, you will be using the Parrot AR.Drone 2.0
bottom-facing camera (64
◦
diagonal FOV, 640px
×
360px) data to
detect and locate targets of interest on the ground.
▶
The data from the Vicon motion capture system and the
images from the quadrotor will be used to find each target’s
location on the ground within the inertial Vicon fixed
reference frame.
▶
This lab requires you to design an image processing algorithm
that will assign georeferenced coordinates to pixels of an
image based on quadrotor position and attitude.
▶
In preparation for the project and the fourth lab, it is highly
encouraged for you to conduct image processing in real-time
using OpenCV, but this is not necessary to complete this lab.
▶
Implementation of a georeferencing algorithm that analyzes
images and AR.Drone pose data to determine the coordinates
of each target of interest.
12
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AER1217 - Autonomy of UAS
Course Information
CARRE
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Spring 2022
Lab 4: Autonomous Drone Localization
You are given a bag file of flight data from the ARDrone Gazebo
Simulator. Similar to Lab 3, the dataset contains the ARDrone
bottom images, vicon pose, and commands. The bottom camera
images contain obstacle indicators and photos of notable
landmarks in Toronto. Your task is to determine the size and
position of the obstacle indicators and the poses of the landmark
photos for the second phase of the project.
13
AER1217 - Autonomy of UAS
Course Information
CARRE
©
Spring 2022
Project: Autonomous Drone Geodashing
In the final project with the information gathered in Lab 4, you are
to generate a desired optimized path to visit all the landmarks in a
predefined sequence, while avoiding obstacles in the environment.
The obstacles will be located at the red targets.
14
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