3 credits
Fall 2025 Lecture Upper Division
Any intelligent robot system interacting with our environment needs to have perception, planning, and control methods in its cognition process. The perception module outlines the robot's procedures to gather and interpret sensory observations into world models. The underlying planning and control modules use those world models to plan robot behaviors and their interaction with our natural environments. Therefore, this course will cover the fundamental topics in robot perception, planning, and control to design general-purpose robot cognition algorithms. Overall, this course is divided into four modules: Robot perception: This covers fundamental techniques needed for robot localization and mapping from raw 3D sensory data. Robot planning: This module will discuss robot behavior planning techniques such as A*, RRT*, and trajectory optimization. Robot Control: This introduces basic control techniques such as PID controller to execute the robot's planned behaviors in the real world. Robot Learning: This part will briefly introduce machine learning techniques for robot decision-making and control.
Learning Outcomes1Formulate and solve the robot perception problems.
2Identify the robot constraints, define their degree of freedom, and formulate their planning and control problems.
3Apply classical and modern robot planning and control techniques to complex robot systems like manipulators, autonomous cars, etc.
4Identify limitations in existing classical robot algorithms and understand their formal guarantees.
5Evaluate and assess current best practices and mechanisms for robot programming.
6Develop a skill for robot programming from perception to low-level control using standard methods.
Prerequisites
made by Purdue students.