AER1217S2022outline_handout

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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 © 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