E28: Mobile Robotics

Fall 2012

Tue, Thu 11:20-12:35, Martin 213
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.

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.

Class Schedule

Week Dates Topics Readings Labs & HW
1 Sep 4, Sep 6

Introduction

  • Math review
  • Robot motion basics
Syllabus
Notes (Ma)
Notes (Sullivan)
Homework 1
2 Sep 11, Sep 13

Robot motion basics, cont'd.

  • Differential drive
  • Integrating equations of motion
  • Pose networks

Perception & action

  • Braitenberg vehicles
  • Behavior based robotics
  • Subsumption architectures
  • State machines
Braitenberg
Aiken
Notes (Bailey)
Worksheet 1
Vehicle sim
Lab 1a
Starter code
3 Sep 18, Sep 20

Sensors

  • passive vs. active
  • cameras
  • Kinect & other 3D sensors

Actuators

  • Types of motors
  • Torque, power, and speed
  • Hydraulics, pneumatics, etc.
Sensors
Motors
Notes (Aaron)
Notes (Abel)
Homework 2
Lab 1b
Starter code
4 Sep 25, Sep 27

Kinematics & dynamics

  • Configuration space
  • Kinematics of wheeled systems
Notes (Shaban)
Notes (Nahmias)
Worksheet 2
Tricycle diagram
5 Oct 2, Oct 4

Kinematics & dynamics continued

  • Integrating equations of motion
  • Serial manipulator kinematics
  • Exam 1: date and time TBA
Notes (Cheney)
6 Oct 9, Oct 11

Control

  • Feedback control
  • Pure pursuit
  • Continuous time linear systems
  • PD control
Notes (Boutelle)
Notes (Zhai)
Homework 3
Lab 2 handout
Lab 2 starter code
FALL BREAK
7 Oct 23, Oct 25

Control, continued

  • PID control
  • Discrete time systems and controllability
  • LQR control
Notes (Hwang)
Notes (Ramirez)
8 Oct 30, Nov 1

Navigation

  • Map representations
  • Navigation via graph search
  • Dijkstra's algorithm and A*
Notes (Spagnolo)
Worksheet 3
9 Nov 6, Nov 8

Uncertainty

  • Probability basics
  • Bayes Rule
  • Introducing the Bayes Filter
Notes (Martin)
Homework 4
Bayes filter demo (MATLAB)
10 Nov 13, Nov 15

Localization

  • Bayes filter
  • Particle filter
  • Exam 2: date and time TBA
Notes (Khaselev)
Lab 3 handout
Final project
11 Nov 20

Kalman filters

  • Traditional Kalman Filter
  • Extended Kalman Filter
Notes (Welkie)
12 Nov 27, Nov 29

Mapping

  • Evidence grids
  • Scan matching
Notes (Zhai)
Notes (Boutelle)
Filters code
Homework 5
13 Dec 4, Dec 6

Student presentations

14 Dec 11

Student presentations

FINAL EXAM - date and time TBA