Course Description
Introduction to the hardware and software involved in autonomous mobile robotics. Course content emphasizes the mathematical and statistical models related to robotic perception and motion, associated algorithms, and their programming in computer- simulated environments. Course structure involves classroom instruction, written and programming assignments, and exams.
Additional Requirements for Graduate Students:
Each written assignment and programming project in this course
will have additional questions for graduate students. These
questions will require the student to read and understand
research papers, write answers that will call upon the student to
critique existing methods and propose improvements, or use the
research in implementing new solutions. Exams will have
additional essay-type questions that will require the graduate
student to find limitations of existing techniques and propose
new approaches. All graduate students will be evaluated in a pool
that is separated from undergraduate students in the course, and
the grade cutoffs will be stricter, thereby implying higher
expectations of accomplishment from graduate students.
Athena Title
Introduction to Robotics
Undergraduate Prerequisite
(CSCI 2610 or CSCI 2611) and CSCI 2720
Graduate Prerequisite
Permission of department
Semester Course Offered
Not offered on a regular basis.
Grading System
A - F (Traditional)
Course Objectives
Students completing "Introduction to Robotics" will gain a deep understanding of the hardware and software involved in robotics, with a focus on programming algorithms. They will understand the past, present, and future of robotics and the main challenges that make robotics difficult. Students will learn various mathematical and statistical models, associated algorithms, and their implementations which are making modern-day robotics possible. The course will consist of lectures and breakout sessions. Students will be graded on the standard A to F grading scale and will provide course evaluations on the instruction and course content following established Computer Science evaluation procedures.
Topical Outline
I. Overview of Robotics 1. Introduction History, State-of-the-art, and Future 2. Robot Hardware Sensors and Effectors 3. Robotic Software Architectures 4. Probability Theory II. Robotic Perception Under Uncertainty 1. Maps 2. Range Finders Beam models, Likelihood fields 3. Cameras Feature-based Measurement Models III. Robotic Motion Under Uncertainty 1. Kinematics 2. Velocity Motion Model 3. Odometry Motion Model 4. Motion and Maps IV. Localization 1. State Estimation Under Uncertainty 2. Filters Bayes, Kalman, Extended Kalman, and Monte Carlo 3. Taxonomy of Localization Problems 4. Markov Localization 5. Extended Kalman Filter Localization 6. Grid Localization 7. Monte Carlo Localization V. Mapping 1. Occupancy Grid Mapping 2. Learning Inverse Measurement Model 3. SLAM with Extended Kalman Filter
Syllabus