INTELLIGENT ROBOTICS
Introduction to common robot modeling and algorithmic approaches. Topics covered include background and algorithms for robot representations, kinematics, motion planning, perception, state estimation, learning, and control.
Topics
- background and algorithms for robot representations
- kinematics
- motion planning
- perception
- state estimation
- learning
- and control
Topics explicitly listed in official catalogue description
Prerequisites & course information
Requisites: ( COMP SCI 320 or 400 ), ( MATH 320 , 340 , 341 , 345 or 375 ) and ( STAT 311 , 324 , 333 , 340 , 371 , MATH/STAT 309 , 409, 431 , MATH 331 , 531 , I SY E 210 or B M E 325 ) or graduate/professional standing Course Designation: Core GenEd - Natural Science & Wellness + Lab Breadth - Natural Science Level - Advanced L&S Credit - L&S Liberal Arts and Science Repeatable for Credit: No Last Taught: Fall 2026 Learning Outcomes: 1. Describe common components of robot software systems. Audience: Undergraduate 2. Apply classical approaches for robot configurations, kinematics, control, perception, state-estimation, mapping, and planning. Audience: Undergraduate 3. Compare and contrast classical approaches for each layer of a robot's software system. Audience: Undergraduate 4. Evaluate different robot software system designs for a target application. Audience: Undergraduate Learning Outcomes: 1. Describe common components of robot software systems. Audience: Undergraduate 2. Apply classical approaches for robot configurations, kinematics, control, perception, state-estimation, mapping, and planning. Audience: Undergraduate 3. Compare and contrast classical approaches for each layer of a robot's software system. Audience: Undergraduate 4. Evaluate different robot software system designs for a target application. Audience: Undergraduate
Sources
- Course catalogue ↗2026-2027