CS 344R: Robotics
- Fall 2005. (unique #54030)
- When/Where: TTh 11:00 - 12:30, PAI 3.20
- Professor Benjamin Kuipers
- Office hours: TTh 10:00 - 11:00 am, TAY 4.130C.
- Teaching Assistant: Patrick Beeson (email@example.com)
- Calculus and basic differential equations
- Good programming skills
- upper-division CS major or equivalent
- This syllabus:
- DRAFT: Some of this may change!
A robot is a computational system coupled with the physical world
through its sensors and effectors. An intelligent robot learns
about its world from experience, and uses the knowledge it accumulates
to make better plans to achieve its goals. Robotics is hard, partly
because it crosses many of the abstraction boundaries that simplify
other areas of computer science.
This course has two goals. First, a number of important theories and
techniques are taught, important to perception and action in a
partially-known world. Second, students will work in teams, applying
these methods to get intelligent behavior from physical robots.
This class will be limited to 36 students, who will be divided into
12 teams of three students, each with a robot to program. If more than
36 students wish to sign up, there will be a waiting list.
A robot is an intelligent system in continuous sensorimotor
interaction with its environment.
Control laws are continuous dynamical systems that can be designed to
bring the robot along a trajectory or keep it near a setpoint in spite
of limited knowledge and sensor and motor errors.
Continuous behavior can sometimes be described in terms of discrete
actions and resulting states. We can make simple topological maps
by defining distinctive states and the actions linking them.
Kalman filters can be used to track a moving target from incomplete
observations, or to track a moving robot while observing fixed
Very general environments can be described by a fine-grained grid,
representing the probabilities that each cell is occupied. (Laser
first, then sonar.)
SLAM (Simultaneous Localization and Mapping)
There is an elegant interaction between the mapping and localization
The overall structure of the environment can be described in a more
concise and useful way using a topological map. Describe places, paths,
regions, boundaries, etc.
Society will be deeply affected if intelligent robots become a reality.
What kinds of implications should we consider?
Assignments will be done in teams of three. Part of the goal of the
course is for you to learn how to work successfully with your team,
solving academic/technical, scheduling/coordination, and
social/interpersonal problems as they arise so that your team is
There will be robot programming assignments due approximately every
two weeks throughout the semester. Each team will have its own robot
to work with.
These assignments may be changed as the course evolves.
Get your robot to move and behave. Implement a simple ``virtual
bump'' sensor using the sonars, and a toy-like wandering control law:
- go straight until bump;
- back and turn;
Build control laws so your robot can use its sonar sensors and camera to:
- continually seek the largest open space;
- follow a wall or corridor;
- follow a brightly-colored object (or person) around.
While using the sonars to avoid obstacles, the robot will use its
camera to identify brightly colored tennis balls (``food''), capture
them, and bring them home to its ``nest.''
Using its camera, the robot will track a tennis ball rolled toward it,
move to catch it, and roll it back. Pair with another team to have
two robots volley the ball back and forth.
Build an occupancy grid metrical map
Using a sensorimotor trace from a robot with a laser range-finder,
including accurate localization of the robot, build a map of the
robot lab and the surrounding corridor.
Simultaneous localization and mapping (SLAM)
Solve the above problem, but without the accurate localization of the
robot. You are given the significantly less accurate odometry
readings, and must use the sensor observations to correct them.
- The assignments will be graded by teams, with all members of
each team receiving the same credit.
(5%, 10%, 10%, 10%, 10%, 10%).
- Individuals will received their own grades for the Midterm (15%)
and Final (20%) exams.
- There will also be credit for Class Participation (10%).
Attendance and Textbook
There is no textbook in this class. Research papers in robotics will be
handed out and distributed online for you to read. Copies of the slides
will be made available, ideally before class each day.
Since so much of the class comes from the lectures and discussion in
class, attendance is required. I will take attendance at the
beginning of the class period each day. (This also helps me learn
your names.) Your attendance will be counted as part of the Class
The Computer Science Department has a Code of Conduct that describes
the obligations of faculty and students. Read it at