Mobile Robotics  16-761

Warning: The following information is for general information only. Each year  the course is changed in different ways to react to student comments from the year before. If you have a CMU andrew account, the latest course  webpage is available here.

Instructor: Alonzo Kelly, NSH 3209Service Robot in Lowes


Prerequisites: There are no formal prerequisites for the course. However, you will get more out of the course if you have done computer vision and robot manipulation courses already. Robotics PhD students are automatically admitted. Other students are admitted subject to approval of the instructor. The curriculum is multidisciplinary. Background in various fields such as computer vision, control theory, robotic manipulation, and computer science will be helpful. Students lacking a sound background in mathematics and programming may not be qualified to take the course.


Text: Kelly, A, Mobile Robotics: Mathematics, Models, and Methods, Cambridge

Lectures: Tues and Thurs noon or afternoon, NSH 3002 (in 2005!)
Spinner Vehicle

Scope: The course covers aspects of perception, planning, control, position estimation, and mechanical configuration that are unique or common to mobile robot systems.


Level: The course is targetted to senior undergraduates and graduate level students. Emphasis will be given to fundamentals.


Grading: All students are graded to an equal standard regardless of the level of their anticipated degree. Grading is based on homework assignments and a term project according to the following breakdown:

Homework - 60%

Term Project - 40%

In some yearsm there has been no project and homework is then worht 100%.

Late Policy: You also have two choices of late policy

Choice 1. Any material handed in late will be subject to a grade reduction of 25% per day up to a maximum of 3 days. All material is due at the start of class. This policy makes it possible for us to hand out solutions to problems and go over them in class.

Choice 2. Anything can be handed in at any time up to the last day of classes but solutions will not be provided.


Hopefully, when this course is over, you will:

 Perceptor Vehicle


A few homework tasks will be assigned which are intended to expose the student to the hands on details of core ideas. There will be homework every 2-3 weeks and it should take about 12-18 hours for a well prepared student to do well on it.


Subject to our ability to make it happen, an updated version of the mobile robot simulator that has been used in the past will be made available. This will enhance the quality of the course, but it has to be available to everyone. Access to a Windows computer and Microsoft Visual C++ will be necessary for some homeworks. Alternately, we will port the simulator to Java and use public domain development tools but the real-time aspects of some homeworks may suffer.

Term Project

Term projects will take the form of a substantial assignment on a topic very relevant to mobile robots. The project is intended to expose students to more detail in at least one area related to the field.


The lectures will develop the fundamentals of this emerging sub-field of robotics by calling on the experience of practitioners, the common themes of the literature, and relevant material from more basic fields such as computer vision, mathematics, and physics.

Lecture Schedule

The order of the lectures follows one typical implementation path for mobile robots from simple to more complex capabilities. The first step, at least for a retrofitted vehicle, is to render the actuators computer controllable with hardware and software. Then, the vehicle can move. Next position feedback is provided by the addition of hardware and software. At this stage, the vehicle can follow a predetermined path "blindly". With the addition of perception hardware and software, the vehicle is now able to avoid obstacles, use landmarks for positioning, and follow environmental features such as a road. Finally, with the addition of strategic planning capabilities, the system can navigate freely and perform a complex function. The last part of the course is devoted to the case where a vehicle is custom designed to be a robot to serve a particular need.


In order to allow for student involvement, this schedule is subject to modification . Topics may be added, deleted, or moved as necessary to accomodate various constraints that cannot be predicted.

16-899A - Lecture Schedule for Spring 2004





Jan 10


  • Course Outline: Intro to course
  • Intro 1: Intro to Mobile Robots
  • Kin 1: Orthogonal Transforms


Jan 17


  • Kin 2: Kinematics of Mechanisms
  • Kin 3: Kinematics Models of Sensors and Actuators
  • Kin 4: Transform Graphs & Pose Networks

Martin Luther King Day !!!

Jan 24


  • Unc 1: Fundamentals of Uncertainty
  • Unc 2: Combining Uncertain Measurements
  • Unc 3: Kalman Filters

Project Topic Selection

HW 1 out

Jan 31


  • Unc 4: Applications of Bayes Rule (?)
  • Unc 5: Particle Filters and Monte Carlo Techniques (+)
  • Dyn 1: Aspects of Ground Vehicle Dynamics
  • Dyn 2: Linear Systems Theory and Stochastic Calculus

HW 1 in

Feb 7



  • Pos 1: Physics of Measurement
  • Pos 2: Mathematics of Position Estimation

HW 2 Out

Feb 14



  • Pos 3: Sensors for Position Estimation
  • Pos 4: Inertial Navigation Systems
  • Pos 5: Satellite Navigation Systems

HW 2 in

Feb 21

Student Presentations



Feb 28

Student Presentations/Midterm Exam



Mar 7-11

Spring Break


midterm grades due Mar 7, 9 pm

Mar 14


  • Ctr 1: Hierarchical Control / Motive Autonomy
  • Ctr 2: Kinematics of Wheeled Mobile Robots
  • Ctr 3: Trajectory Generation
  • Ctr 4: Obstacle Avoidance
  • Ctr 5: Path and Trajectory Following (+)

Project Proposals due

Mar 21


  • Per 1: Mathematics for Perception (+)
  • Per2: Physics of Radiative Sensors
  • Per 3: Sensors for Perception
  • Per 4: Perception Algorithms
  • Per 5: Visual Tracking & Servoing (+)

HW 3 out

Mar 28


  • Map 1: Intro to Maps and Representation
  • Map 2: Perception Based Localization
  • Map 3: Globally Consistent Mapping
  • Map 4: SLAM
  • aaron out on 30th

HW 3 in

Apr 4


  • Pln 1: Introduction to Motion Planning
  • Pln 2: Algorithms for Motion Planning
  • Pln 3: Real-Time Planning & Dynamic Environments.
  • Pln 4: Nonholonomic Motion Planning (?)
  • Pln 5: Traffiic Scheduling(+)

HW 4 out

Apr 11


  • Sys 1: Simulating Motion Sensing
  • Sys 2: Simulating Environmental Sensing
  • Calibration
  • Robotics in Industry?

HW 4 in

Apr 18




Apr 25



classes over April 29

Final grades due May 12

May 5-10



Final Exam Week

More on Term Projects

This year, term projects will be done in collaborative groups. Groups should include 5 people with deviations permitted for class sizes which are not multiples of 5. If you want to work with specific individuals you should make sure that all of you represent consistent project interests. We will ask each of you to rank order a set of project topics and then form you into groups based on your responses.

Groups are expected to organize themselves into an efficient team, elect a leader or come up with whatever strategy you need to resolve disputes, and to meet regularly to coordinate yourselves and update each other on status.

All term projects require groups to prepare an interim presentation (literature survey / tutorial) and to present and submit a final report. The topic does not necessarily have to be an area of active research but it does have to be a believable part of a core mobile robots university curriculum. Each project will involve two steps.

You must rank order topics not later than Feb 7, (all you have to do is decide your preference). Survey presentations take place Feb 21 or Feb 28. Final reports and presentations of their contents are dues Apr 25.

Here is a tentative list of suggested topics. I will fill in the details later. Each group will be assigned to the instructor or TA in order to mentor you in the research process.

Specific Ideas for Topics
  • Math and Physics
Finer Aspects of Kalman Filtering
  • Physics
Physics of Vehicle Rollover (Nonsteady turn case)
  • Perception
Treatise on Correspondence and the Revisiting Problem
  • Control
Coordinated Control of Mobile Robots
  • Localization
Map Insertion - The Kidnapped Robot Problem
Error dynamics of Odometry Damped Inertial Guidance
  • Planning & Scheduling
Nonholonomic Motion Planning (if not covered in class)
Traffic Scheduling of Automatic Guided Vehicles