E28: Mobile Robotics

Fall 2016

Lecture: T/R 11:20AM-12:35PM, Hicks 211
Office hours: M 11:00AM-12:30PM / W 1:30PM-3:00PM

Instructor: Matt Zucker


Course description

This course addresses the problems of controlling and motivating robots to act intelligently. Projects and homeworks will focus on programming both real and simulated robots to execute tasks and to explore and interact with their environment.

Look over the course syllabus for more information.

Please direct all class-related communications to the course Piazza. Your participation in Piazza will help the entire class share knowledge and tips, and it also counts towards your participation grade.


Class schedule

The topics below are subject to change. As we move through the course, I will update the list to reflect the new schedule, readings, and assignments.

Week Dates Topics Reading Labs & HW
1 Aug 30, Sep 1
  • Introduction
    • Math review
    • Orthogonal transformations
    • Rigid transformations
  • Robot motion basics
    • Differential drive
    • Integrating equations of motion
2 Sep 6, Sep 8
  • Pose networks
    • Relating many coordinate frames
    • Composing chains of transforms
  • Control strategies
    • State machines
    • Artificial potential fields
3 Sep 13, Sep 15
  • Sensors
    • Passive vs. active
    • Cameras
    • Kinect & other 3D sensors
  • Actuators
    • Types of motors
    • Torque, power and speed
    • Hydraulics, pneumatics, etc.
4 Sep 20, Sep 22
  • Kinematics & dynamics
    • Configuration space
    • Kinematics of wheeled systems
    • Integrating equations of motion
5 Sep 27, Sep 29
  • Serial manipulator kinematics
    • Forward kinematics
    • Inverse kinematics
    • Kinematic Jacobian
6 Oct 4, Oct 6
  • Control
    • Feedback control
    • PD control
    • PID control
Fall break
7 Oct 18, Oct 20
  • Navigation
    • Map representations
    • Navigation via graph search
    • Dijkstra's algorithm and A*
8 Oct 25, Oct 27
  • Intro to probabilistic robotics
    • Probability basics & Bayes' rule
    • Applications: localization, mapping, SLAM
  • Bayes filter
9 Nov 1, Nov 3
  • Probability fundamentals, cont'd.
    • Continuous random variables
    • Sampling from distributions
  • Particle filter
10 Nov 8, Nov 10
  • Range sensor models for localization
11 Nov 15, Nov 17
  • Probability fundamentals cont'd. cont'd.
    • Expected value
    • Variance and covariance
  • Kalman Filter
12 Nov 22
  • Extended Kalman Filter
Thanksgiving break
13 Nov 29, Dec 1
  • TBA
14 Dec 6
  • TBA