Difference between revisions of "CISC367 S2023"

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|Kinematics/Dynamics<!--ROS/Gazebo-->
 
|Kinematics/Dynamics<!--ROS/Gazebo-->
 
|URDFs, ROS <tt>tf2</tt>, odometry<!--Odometry, ground truth positions-->
 
|URDFs, ROS <tt>tf2</tt>, odometry<!--Odometry, ground truth positions-->
|[https://docs.ros.org/en/humble/Tutorials/Intermediate/Tf2/Introduction-To-Tf2.html Introduction to tf2] <!-- [https://docs.ros.org/en/humble/Tutorials/Advanced/Simulators/Gazebo.html robot_state_publisher]-->
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|[https://docs.ros.org/en/humble/Tutorials/Intermediate/Tf2/Introduction-To-Tf2.html Introduction to tf2], [http://nameless.cis.udel.edu/class_data/cisc367_s2023/mr_odometry.pdf] <!-- [https://docs.ros.org/en/humble/Tutorials/Advanced/Simulators/Gazebo.html robot_state_publisher]-->
 
|[https://docs.google.com/presentation/d/1HLQ4T4XW0T3jS112b8XO9CeJV1pmjZQVmhG-mPCn0gU/edit?usp=sharing slides]<br>''HW #1 due''<!--[https://docs.google.com/presentation/d/1j-oEgsL4DRU_EwcmBwKiCwmEYsKa3owOYrnZaMtW1Rg/edit slides] -->
 
|[https://docs.google.com/presentation/d/1HLQ4T4XW0T3jS112b8XO9CeJV1pmjZQVmhG-mPCn0gU/edit?usp=sharing slides]<br>''HW #1 due''<!--[https://docs.google.com/presentation/d/1j-oEgsL4DRU_EwcmBwKiCwmEYsKa3owOYrnZaMtW1Rg/edit slides] -->
 
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Revision as of 08:02, 17 February 2023

Course information

Title CISC367-012 Introduction to Mobile Robot Programming
Description A hands-on approach to implementing mobile robot algorithms on a small wheeled platform, both in simulation and reality. We will review the fundamentals of kinematics, planning, sensing, and control, as well as getting acquainted with higher-level concepts related to navigation, tracking, mapping, and learning.
When Wednesdays and Fridays, 8:40-9:55 am. When there is a homework due, no more than the first 30 minutes of each class will be in lecture format. The rest of the class period (and optionally the subsequent office hours) will be spent working on the robots.
Where Feb. 8: Willard 215

Feb. 10 and beyond: Smith 211

Instructor Christopher Rasmussen, 446 Smith Hall, cer@cis.udel.edu
Office hours Mondays, 9-10:30 am and Wednesdays and Fridays, 9:55-11 am in Smith 211 (starting Feb. 10)
Grading
  • 90% 6 programming homeworks
  • 10% 2 quizzes

Programming assignments will be graded on how many of the subtasks you complete or demonstrate successfully.

For the overall course grade, a preliminary absolute mark will be assigned to each student based on the percentage of the total possible points they earn according to the standard formula: A = 90-100, B = 80-90, C = 70-80, etc., with +'s and -'s given for the upper and lower third of each range, respectively. Based on the distribution of preliminary grades for all students (i.e., "the curve"), the instructor may increase these grades monotonically to calculate final grades. This means that your final grade can't be lower than your preliminary grade, and your final grade won't be higher than that of anyone who had a higher preliminary grade.

I will try to keep you informed about your standing throughout the semester. If you have any questions about grading or expectations at any time, please feel free to ask me.

Academic policies Programming projects should be demo'd in class on the deadline day and uploaded to Canvas by midnight of that day (with a grace period of a few hours afterward...after sunrise is definitely late). A late homework is a 0 without a valid prior excuse. To give you a little flexibility, you have 3 "late days" to use on homeworks to extend the deadline to the next class period without penalty. No more than one late day may be used per assignment. Late days will automatically be subtracted, but as a courtesy please notify the instructor in an e-mail of your intention to use late days before the deadline.

Assignment submissions should consist of a directory containing all code (your .cpp/.py files, etc.), any output data generated (e.g., images, movies, etc.), and an explanation of your approach, what worked and didn't work, etc. contained in a separate text or HTML file. Do not submit executables or .o files, please! The directory you submit for each assignment should be packaged by tar'ing and gzip'ing it or just zip'ing it.

Students can discuss problems with one another in general terms, but must work independently or within their teams as specified for each assignment. This also applies to online and printed resources: you may consult them as references (as long as you cite them), but the words and source code you turn in must be yours alone. The University's policies on academic dishonesty are set forth in the student code of conduct here.

Instructions/Resources

Robot

Yes, our robot platform is a Roomba. Except it can't clean.

  • Platform: iRobot Create 3 running ROS2 Humble
  • SBC: Raspberry Pi 4 B running Ubuntu 22.04 and ROS2 Humble
  • Lidar: Slamtec RPLIDAR A1
  • Camera: RealSense D435
Operating system

Working backwards from the ROS requirements there are 3 options for your laptop...

  • Linux: Ubuntu Desktop 22.04 LTS. Installation instructions
  • Windows: Version 10.
  • MacOS: Mojave 10.14. Several students have had success on HW #1 by installing Ubuntu in a UTM VM. Bridging mode was necessary for wifi
ROS2

We are using ROS2, to be exact, and the Humble Hawksbill version. My laptop and the Raspberry Pi's on the robots are running Ubuntu, so you will have your best support for this option. I have personally tried installing ROS2 Humble on Windows and it seems to be fine, so I at least have the ability to test things in this environment. I have no access to a Mac and therefore no experience and no ability to help troubleshoot issues there. A lot of public sample code and tutorials are available in both C++ and Python, but expect mostly C++ examples from me. I am agnostic about which of these two languages you use for homeworks and projects, but you will get your best support from me in C++.

Readings
  • K. Lynch and F. Park, Modern Robotics (MR), 2019
  • P. Corke, Robotics, Vision, and Control (RVC), 2017
  • K. Astrom and R. Murray, Feedback Systems (FS), 2008
  • S. Lavalle, Planning Algorithms (PA), 2006
Gazebo

This is a 3-D robot simulator


Schedule

Note: The blue squares in the "#" column below indicate Wednesdays.

# Date Topic Notes Readings/links Assignments/slides
1 Feb. 8 Introduction Background, course information slides
2 Feb. 10 hello robot, hello ROS ROS basics, Create ROS interface Creating & building ROS2 packages, ROS2 nodes,
ROS2 topics, Create 3 topics
slides
HW #1
3 Feb. 15 Kinematics/Dynamics Degrees of freedom, configuration space; wheeled systems (unicycle/car vs. differential drive) MR Chap. 2, PA Chap. 13.1.2

slides

4 Feb. 17 Kinematics/Dynamics URDFs, ROS tf2, odometry Introduction to tf2, [1] slides
HW #1 due
5 Feb. 22 Visualization and simulation ROS rviz2; connecting to the lidar sensor rviz2 user manual (Turtlebot3), rplidar_ros HW #2
6 Feb. 24 Visualization and simulation ROS Gazebo Gazebo classic tutorials
7 Mar. 1 Estimation Least-squares line-fitting, Kalman filter,
outlier rejection with RANSAC
Scikit-learn ordinary least-squares and RANSAC
8 Mar. 3 Controllers Basic feedback and PID control concepts FS Chap. 1 HW #2 due
9 Mar. 8 Controllers Waypoint following, wall following, line following RVC Chap. 4-4.1.2
10 Mar. 10 Motion planning ROS nav2 package nav2, Turtlebot3 navigation tutorial HW #3
11 Mar. 15 Motion planning More nav2 details
12 Mar. 17 Motion planning Discrete search, randomized search, path smoothing PA Chap. 1-1.3, MR Chap. 10-10.1 Quiz #1
13 Mar. 22 Localization Particle filters, MCL Thrun particle filtering slides HW #3 due
14 Mar. 24 Mapping Occupancy grids with known robot poses Abbeel slides, adapted from Probabilistic Robotics
Mar. 29 NO CLASS
Spring break
Mar. 31 NO CLASS
Spring break
Apr. 5 NO CLASS
Instructor away
15 Apr. 7 SLAM ROS slam_toolbox package slam_toolbox Github, nav2 Navigating while mapping tutorial HW #4
16 Apr. 12 SLAM Thrun FastSLAM slides
17 Apr. 14 Computer vision Connecting to the RGB-D camera realsense-ros Github HW #4 due
18 Apr. 19 Computer vision Color, AprilTags AprilTag Github, apriltag_ros HW #5
19 Apr. 21 Computer vision Off-the-shelf object detectors ROS2 wrapper around YOLOv5 (not tested)
20 Apr. 26 Computer vision Training your own object detector Training YOLOv5 on custom data
21 Apr. 28 3-D point cloud processing Obstacles as fitted ground plane outliers; ROS perception_pcl package perception_pcl HW #5 due
22 May 3 Miscellanous HW #6
23 May 5 Learning Dynamical tasks, reinforcement learning Gymnasium Github
24 May 10 Ethics & societal issues Professional responsibilities, robots as caregivers and soldiers ACM, IEEE codes of ethics; proposed code for robotics engineers;
"The Ethical Frontiers of Robotics", N. Sharkey (2008)
Quiz #2
25 May 12 FULL PERIOD FOR DEMOS/COMPETITION HW #6 due
NO FINAL EXAM