CS499 Section 002
Autonomous Robotics

Instructor Sean Luke, 415 S&T II, 3-4169
Prerequisites CS 310, ECE 303, Math 203, Math 114
Very Strongly Recommended: Math 213, CS 367 (or knowledge or C)
Don't let the prereqs scare you. We just want to make sure students have a good background to do robotics.
Office Hours TBA
Meets Innovation Hall Room 105, Tuesdays, 4:30 pm - 7:10 pm
Sometimes meets in lab: Research I 470

About the Course

This course will cover various topics in autonomous robotics, including architectures, basic kinematics, basic controls, reactivity, planning, simulation and modeling, sensing and locomotion, and multiagent environments. The course will cover both high-level abstract topics and nuts-and-bolts of robot construction, including some simple sensor and microelectronics and programming.

The course will include labs involved in construction and programming of a mobile autonomous robot. Depending on class size, such construction and programming may be done by teams. Programming will be done in Java and C.

Further information will appear on the Course Web Page


There are no good general texts on autonomous robotics. They're all terrible. So we're going with the cheapest terrible option: Computational Principles of Mobile Robotics by Gregory Dudek and Michael Jenkin. Get the softcover version. We will also be using various lecture notes and chapters from other texts as necessary.

Tentative Class Schedule

This class schedule is certain to change significantly. It's primary function here is to give you an idea of what the course might entail. There are fourteen weeks all told:
  1. Introduction, Braitenberg Vehicles, basic concepts in autonomous agents, laboratory, robot construction
  2. Microelectronics, Sensors, Effectors, Mechanics
  3. Reactive Control
  4. Kinematics
  5. Controls and Pose Maintenance
  6. Multi-agent Robotics
  7. Midterm. Special lecture: Computer Vision
    (Spring Break)
  8. Search, Probability
  9. Localization
  10. Path Planning
  11. Partial-Order Planning, Multi-agent Planning, Robot Architectures
  12. Reinforcement Learning
  13. Neural Networks
  14. (Final Project Presentations)

Grading Policies

This course will consist of homework and projects, possibly including a final project, and two exams. The breakdown will be approximately:

Homework and Projects50% with higher weight given to harder projects (the final project may count for half of the weight or more)
Exams25% Each

There will be no make-up tests for missed examinations. Late homework will be accepted but at a loss of 20% per day (homework later than 4 days, or after the last day of class, is worth nothing).