Course Description
Fundamental robotics concepts, hardware, and theory as well as hands-on skills on embedded systems and robotic operating system (ROS). Robotics concepts such as kinematics, locomotion, perception, computer vision, localization and mapping, machine learning, and related hardware.
Additional Requirements for Graduate Students:
Graduate students have additional homework and project
requirements that challenge the student to go deeper into the
course subject. These requirements can include creating and
executing a project independently, regular progress reports
about the project, and the use of grading standards/rubrics that
account for the maturity, sophistication of design ability, and
increased engineering accountability. Specifically, graduate
students will be led to design advanced robotics
algorithms/systems and perform scientific evaluations. The
additional work may include: 1. Each homework will have 1-2
extra advanced questions; 2. Course project will have scientific
research, literature survey, and extensive theoretical/empirical
evaluation requirement.
Athena Title
Intro to Robotics Engineering
Prerequisite
(ECSE 2170-2170L or ECSE 2170H or ENGR 2170-2170L or ENGR 2170E) and (CSCI 1301-1301L or CSCI 1301E or ELEE 2040 or INFO 2000 or INFO 2000E) and (MATH 2250 or MATH 2250E or MATH 3000)
Semester Course Offered
Offered every year.
Grading System
A - F (Traditional)
Course Objectives
Upon completion of this course, students should be able to: • Understand fundamental robotics concepts and their applications in robotic arms and autonomous mobile robots • Write programs and launch files in the robotic operating system (ROS) • Design a robotic system by applying the principles learned in the class
Topical Outline
• Overview of robotics and ROS • Robotic hardware and software locomotion • Kinematics: forward and inverse kinematics, differential kinematics: robotic arms and wheeled robot • Robotic control • Perception: sensors and computer vision • Embedded systems for robotics (Raspberry Pi and Arduino) • Machine learning • Navigation: localization, mapping, Kalman filter, SLAM • Path and trajectory planning • Ethical, legal, and social implications of robotic technologies
Syllabus