Course Description

This course introduces students to foundational principles of mobile robotics with a focus on developing software for robots. Students will gain hands-on experience with ROS2 (Robot Operating System 2) and implement core algorithms for robot perception, navigation, and sensor fusion. Through practical coding exercises and real sensor data processing, students will learn how robots perceive, navigate, and interact with their environment.

Topics covered include dynamic modeling, coordinate transformations, sensor processing (IMU, GPS, wheel encoders, LiDAR, cameras), sensor fusion algorithms including Kalman filters, Simultaneous Localization and Mapping (SLAM), and feedback control for robotics. The course emphasizes practical implementation and real-world applications in autonomous systems.

Learning Objectives

By the end of this course, students will be able to:

• Design and implement ROS2 nodes for robot control and perception
• Develop sensor fusion algorithms using Extended Kalman Filters
• Process and integrate data from multiple sensors (IMU, GPS, encoders, LiDAR)
• Implement SLAM algorithms for robot localization and mapping
• Apply coordinate transformations and quaternion operations in robotics
• Create motion control algorithms for mobile robots
• Evaluate and optimize robot navigation systems

Prerequisites

Students are expected to have the following background:

• Mathematics: MATH 221 (Calculus), MATH 225 (Linear Algebra), MATH 285 (Differential Equations)
• Statistics: STAT 400 or equivalent
• Programming: CS 125, CS 225 or equivalent - proficiency in Python required
• Note: Graduate standing is sufficient to waive prerequisite requirements for graduate students

Important Dates

Grading Distribution

Grading Scale