Difference between revisions of "CISC367 S2023"
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|<!--[http://nameless.cis.udel.edu/class_data/cisc829/oct25/engelhard_rgbd_slam_slides.pdf RGBD SLAM slides], [http://nameless.cis.udel.edu/class_data/cisc829/oct25/endres12icra.pdf ICRA 2012 RGBD SLAM paper], U. Texas slides on SIFT features [http://nameless.cis.udel.edu/class_data/cisc829/oct25/grauman_lecture15_local_features.pdf 1], [http://nameless.cis.udel.edu/class_data/cisc829/oct25/grauman_lecture16_bow.pdf 2]--> | |<!--[http://nameless.cis.udel.edu/class_data/cisc829/oct25/engelhard_rgbd_slam_slides.pdf RGBD SLAM slides], [http://nameless.cis.udel.edu/class_data/cisc829/oct25/endres12icra.pdf ICRA 2012 RGBD SLAM paper], U. Texas slides on SIFT features [http://nameless.cis.udel.edu/class_data/cisc829/oct25/grauman_lecture15_local_features.pdf 1], [http://nameless.cis.udel.edu/class_data/cisc829/oct25/grauman_lecture16_bow.pdf 2]--> | ||
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Revision as of 15:08, 7 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 | Wednesdays and Fridays, 9:55-11 am in Smith 211 (starting Feb. 10) |
| Grading |
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 are due by midnight of the deadline 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 6 "late days" to use on homeworks to extend the deadline by one day each without penalty. No more than three late days 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 files, makefile, 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. The resulting file should be e-mailed to the instructor (cer@cis.udel.edu). Students can discuss problems with one another in general terms, but must work independently on programming assignments. 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
| Robot |
Yes, our robot platform is a Roomba. Except it can't clean.
|
| Operating system |
Working backwards from the ROS requirements there are 3 options for your laptop...
|
| 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++.
|
| Gazebo |
This is a 3-D robot simulator |
Schedule
Note: The blue squares in the "#" column below indicate Wednesdays.
| # | Date | Topic | Notes | Readings | Assignments/slides |
|---|---|---|---|---|---|
| 1 | Feb. 8 | Introduction | Background, course information | ||
| 2 | Feb. 10 | hello robot, hello ROS | ROS packages, nodes, topics | HW #1 | |
| 3 | Feb. 15 | ||||
| 4 | Feb. 17 | HW #1 due | |||
| 5 | Feb. 22 | ROS tf2, rviz, Gazebo | HW #2 | ||
| 6 | Feb. 24 | ||||
| 7 | Mar. 1 | ||||
| 8 | Mar. 3 | HW #2 due | |||
| 9 | Mar. 8 | ||||
| 10 | Mar. 10 | ROS nav2 | HW #3 | ||
| 11 | Mar. 15 | ||||
| 12 | Mar. 17 | ||||
| 13 | Mar. 22 | Localization | HW #3 due | ||
| 14 | Mar. 24 | SLAM | |||
| Mar. 29 | NO CLASS Spring break |
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| Mar. 31 | NO CLASS Spring break |
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| Apr. 5 | NO CLASS Instructor away |
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| 15 | Apr. 7 | SLAM | ROS slam_toolbox | HW #4 | |
| 16 | Apr. 12 | ||||
| 17 | Apr. 14 | Computer vision | Color | HW #4 due | |
| 18 | Apr. 19 | Computer vision | Object detection | HW #5 | |
| 19 | Apr. 21 | 3-D point cloud processing | Obstacles as ground plane fit outliers | ||
| 20 | Apr. 26 | ||||
| 21 | Apr. 28 | HW #5 due | |||
| 22 | May 3 | HW #6 | |||
| 23 | May 5 | ||||
| 24 | May 10 | Learning | Reinforcement learning | ||
| 25 | May 12 | HW #6 due | |||
| NO FINAL EXAM |
