Lecture Slides for Robotics and Intelligent Systems

Robert F. Stengel

Princeton University
School of Engineering and Applied Science
Department of Mechanical and Aerospace Engineering

Robotics and Intelligent Systems is an undergraduate course that presents the theory of robotic and intelligent systems. Particular attention is given to modeling dynamic systems, measuring and controlling their behavior, and making decisions about future courses of action. The content is necessarily broad, and the course level is both introductory and intense. The intent is to motivate and prepare students to conduct research projects and to pursue further study.

Twenty-four 80-minute lectures were given during the term (see Syllabus). The slides used as lecture materials are presented here. The portable document files may be downloaded for non-commercial, educational use only, with acknowledgment of the source. Several graphics found on the web are included without attribution. Any graphic material that is deemed to infringe on another's copyright will be promptly removed upon formal notification by the copyright holder.

Lecture Slides

• Lecture 1: Overview and Preliminaries
• Lecture 2: Mobile Robots, Position, and Orientation
• Lecture 3: Translational and Rotational Dynamics
• Lecture 4: Flying and Swimming Robots
• Lecture 5: Articulated Robots
• Lecture 6: Transformations, Path Planning, and Trajectories
• Lecture 7: Time Response of Dynamic Systems
• Lecture 8: Dynamic Effects of Feedback Control
• Lecture 9: Control Systems
• Lecture 10: Sensors and Actuators
• Lecture 11: Introduction to Optimization
• Lecture 12: Numerical Optimization
• Lecture 13: Dynamic Optimal Control
• Lecture 14: Formal Logic, Algorithms, and Incompleteness
• Lecture 15: Computers, Computing, and Sets
• Lecture 16: Probability and Statistics
• Lecture 17: Machine Learning
• Lecture 18: Introduction to Neural Networks
• Lecture 19: Neural Networks
• Lecture 20: Information, Search, and Expert Systems
• Lecture 21: State Estimation
• Lecture 22: Stochastic Control
• Lecture 23: Parameter Estimation and Adaptive Control
• Lecture 24: Task Planning and Multi-Agent Systems