Course Description: This course is intended to be a comprehensive introduction to robotics algorithms for a senior undergraduate/first-year graduate student. It is a Computer Science course, and introduces the student to well-known algorithms in making a simple robot autonomous.
This Fall, the course is modified to follow a flipped classroom model. To accommodate students with limited programming background and Linux experience, we will have activities in-class and videotaped lectures. Students are expected to watch videos at their leisure (before class) and attend class where there will be quizzes on material relevant to the various weeks. The in-class activities will be hands on and you will submit your work at the end of the class via AutoLab. There will be six programming assignments (PA) spread through the semester that will require longer programming effort from you. All quizzes, in-class activities and PAs will be graded. There will be no mid-term or finals in this class.
Prerequisite(s): Engineering Probability, Data Structures, and Programming in C++/Python/Java. Knowledge of linear algebra is a plus.
Credit Hours: 3 (468)/ 3 (568)
Textbooks
There are no required textbooks for this class. Material is selected from a collection of reference books. They include:
Introduction to Autonomous Mobile Robots, 2^nd^ Edition
Author(s): Ilah Nourbaksh, Ronald Seigward, Davide Scaramuzza
ISBN-13: 978-0262015356\
Probabilistic Robotics
Author(s): Sebastian Thrun, Wolfram Burgard, Dieter Fox
ISBN-13: 978-0262201629\
Introduction to Autonomous Robots
Authors: Correll, Nikolaus and Hayes, Bradley, and Heckman, Christoffer, and Roncone, Alessandro
Freely available online in the link provided.\
Planning Algorithms
Authors: Steve Lavelle
Freely available online.\
Course Objectives:
At the completion of this course, students will be able to:
Mobility: Wheeled locomotion
Kinematics: Wheeled kinematics, coordinate transforms
Sensing: Sensing, sensor classification and sensor characterization
Sensing: Various types of sensors
Sensing: Range sensing and line fitting algorithms, range histograms
Sensing: Vision, image formation, camera model, perspective projection
Sensing: Image processing, SIFT
Sensing: Structure from stereo
Estimation: Probabilistic modeling of robot localization and mapping
Estimation: General Bayes’ formulation of reasoning under uncertain conditions
Estimation: Specific formulations of localization and mapping
Planning: Given a map, understand how a robot can navigate from a location to another location
Planning: Randomized algorithms for planning
Learning: Reinforcement Learning basics for planning
Learning: Deep learning basics for perception
Task Allocation: In multi-robot systems, study algorithms that allow robots to perform task allocation
Applications: Study full multi-robot systems in deployment/research that combine all the algorithms learnt in this class and beyond
While this is a reasonable outline, the instructor reserves the right to change the syllabus as he sees fit. Please check the course site for the latest changes.
Grade Distribution:\
Programming Assignments/Homeworks 60% In-class quizzes 20% In-class activities 20% Class and Piazza partcipation 5% ———————————– —–
Grades will be on a curve. From prior experience, I have seen different performance from the undergraduate (468) and graduate (568) students. Therefore, for fairness, I’ll grade students on two separate curves, one for graduate students and another for the undergraduate students.
Course Policies:
General No makeup quizzes or in-class activities will be given unless discussed on a per case basis well in advance.
Grades
Graded on a curve with B+ roughly being the median.
Grades will be maintained in the myUB course system. Students are responsible for tracking their progress by referring to the online gradebook and report any discrepancies.
Labs and Assignments
Students are expected to work independently. Offering and accepting solutions from others is an act of plagiarism, which is a serious offense and all involved parties will be penalized according to the Academic Honesty Policy. Discussion amongst students is encouraged, but when in doubt, direct your questions to the professor, teaching assistant or graders.
Unless addressed with the professor well ahead of a deadline, late submissions will not be allowed.
Attendance and Absences
Attendance is expected each class. Participation in class and on piazza carries 5% of the grade. Activities and quizzes are in-class. The professor will not entertain any grade changes toward the end of the course if the student has not participated during the semester.
If you have a medical reason for missing class, I will require a doctor’s note. Without clear directions from a doctor asking time off for you, I will assume you are able to do the class work and attend class.
Students are responsible for all missed work, regardless of the reason for absence. It is also the absentee’s responsibility to get all missing notes or materials.
Plagiarism Policy:
This course has several programming assignments and in-class activities mostly based on the ROS programming environment. We use sophisticated code checkers to check for code copied from assignments from this class as well as submissions from prior editions of the class. It is very improbable that you will be able to fool the code checker.
Along the same lines, if you see someone else’s code to understand the logic, it is probable that our code checker will flag this as plagiarized since your code will be influenced by what you saw and will look structurally similar.
Discussing high-level programming ideas with other students is acceptable. However, this is a slippery slope, and the more detailed these discussions are and the more they tend toward actual code, the more likely the code checker will flag this as plagiarism.
Copying snippets of code from online resources (such as StackOverflow, GitHub etc) is also considered plagiarism. When you are in doubt, please check with the professor for clarity.
Tentative Course Outline:
The weekly coverage might change as it depends on the progress of the class. However, you must keep up with the reading assignments.