CISC849 F2023

Course information

Title	CISC849-015 Robot Navigation and Autonomy
Description	A hands-on approach to implementing mobile robot algorithms on a small wheeled platform, both in simulation and reality. We will focus on image- and depth-based sensing algorithms for obstacle segmentation, object recognition, and motion planning, including deep learning techniques in these areas.
When	Tuesdays and Thursdays, 2:20-3:40 pm. 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	Smith 211
Instructor	Christopher Rasmussen, 446 Smith Hall, cer@cis.udel.edu
Office hours	Tuesdays and Thursdays, 3:40-5 pm in Smith 211 (starting Aug. 31)
Grading	60% 5 programming homeworks (12% each) 15% 3 quizzes 25% Final project 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
ROS	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 Desktop 22.04 LTS, and I strongly recommend that you do the same. I say this not because of convenience, but after hard experience last semester with another group of students. Trying to install and run ROS2 on Windows or MacOS natively is a recipe for frustration and disappointment. Here are installation instructions for Ubuntu. Several students with MacOS had success using Ubuntu in a VM such as UTM. With a VM there are more issues such as setting up bridging mode for wifi, but it's workable. 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++. ROS2 installation instructions Documentation Tutorials nav2 slam_toolbox
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 Tuesdays.

#	Date	Topic	Notes	Readings/links	Assignments/slides
1	Aug. 29	Introduction	Background, course information
2	Aug. 31	hello robot, hello ROS	ROS basics, Create ROS interface	Creating & building ROS2 packages, ROS2 nodes, ROS2 topics, Create 3 topics	HW #1
3	Sep. 5	Kinematics/Dynamics	Degrees of freedom, configuration space; wheeled systems (unicycle/car vs. differential drive)	MR Chap. 2, PA Chap. 13.1.2
4	Sep. 7	Kinematics/Dynamics	URDFs, ROS rviz2 and tf2, odometry	rviz2 user manual (Turtlebot3), Introduction to tf2, MR Chap. 13.4	HW #1 due
5	Sep. 12	More ROS + Controllers	ROS 2 workspaces and packages, subscribing and publishing, and timer callbacks; basic feedback controller concepts	FS Chap. 1	HW #2
6	Sep. 14	Controllers	Waypoint following, line following, trajectory and wall following (pure pursuit)	RVC Chap. 4-4.1.2
7	Sep. 19	HW #2	Coding time
8	Sep. 21	Costmaps and discrete motion planning	Connecting to the lidar sensor; representing the environment as a map and basic planning	rplidar_ros; MR Chap. 10.4, Abbeel slides (skip SLAM, reflection maps)	HW #2 due
9	Sep. 26	Estimation	Least-squares line-fitting and outlier rejection with RANSAC for lidar scans	Scikit-learn ordinary least-squares and RANSAC	Quiz #1 HW #3
10	Sep. 28	Localization	Particle filters, MCL	Thrun particle filtering slides
11	Oct. 3	Localization
12	Oct. 5	HW #3	Quiz then coding time
13	Oct. 10	SLAM	ROS slam_toolbox package	slam_toolbox Github, Thrun FastSLAM slides	HW #3 due
14	Oct. 12	HW #3			HW #4
15	Oct. 17	Computer vision	Connecting to the RGB-D camera	realsense-ros Github, basic OpenCV color processing
16	Oct. 19	Computer vision	Line finding	Hough lines
17	Oct. 24	Computer vision	Tags/fiducials	AprilTag Github, apriltag_ros
18	Oct. 26	HW #4	Coding time		HW #4 due
19	Oct. 31	HW #4	Coding time		HW #5
20	Nov. 2		Coding time
21	Nov. 7	Computer vision	Getting depth/3-D point cloud from the RealSense	realsense-ros Github, perception_pcl for further analysis
22	Nov. 9		Coding time		HW #5 due
23	Nov. 14	Computer vision	Object detection	YOLOv5 (Python, lots of requirements), Training YOLOv5 on custom data	Final project
	Nov. 16	NO CLASS Instructor away
	Nov. 21	NO CLASS Thanksgiving break
	Nov. 23	NO CLASS Thanksgiving away
24	Nov. 28	Learning	Dynamical tasks, reinforcement learning	Gymnasium Github
25	Nov. 30	Ethics & societal issues	Quiz then coding time		Quiz #3
26	Dec. 5	Final project	Coding time
27	Dec. 7	Final project demos/presentations