CS 547: Sensing and Planning in Robotics

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CS 547 Sensing and Planning in Robotics

• Gaurav S. Sukhatme
• Computer Science
• Robotic Embedded Systems Laboratory
• University of Southern California
• gaurav_at_usc.edu
• http//robotics.usc.edu/gaurav

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Administrative Matters

• Signup - please fill in the details on the signup
  sheet if you are not yet enrolled
• Web page http//robotics.usc.edu/gaurav/CS547
• Email list cs547-usc-fall-2010_at_googlegroups.com
• Grading (3 quizzes 45, class participation 5,
  and project 50)
• TA There is no TA for this class
• Note First quiz today, scores available at the
  end of the week to help you decide if you want to
  stay in the class

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Project and Textbook

• Project
• Team or individual projects
• Equipment (Player/Stage/Gazebo software, ROS,
  Create robots with sensors)
• Book
• Probabilistic Robotics (Thrun, Burgard, Fox)
• Available at the Bookstore

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I expect you to

• come REGULARLY to class
• visit the class web page FREQUENTLY
• read email EVERY DAY
• SPEAK UP when you have a question
• START EARLY on your project
• If you dont
• the likelihood of learning anything is small
• the likelihood of obtaining a decent grade is
  small

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In this course you will

• Learn how to address the fundamental problem of
  robotics i.e. how to combat uncertainty using the
  tools of probability theory
• Explore the advantages and shortcomings of the
  probabilistic method
• Survey modern applications of robots
• Read some cutting edge papers from the literature

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Syllabus and Class Schedule

• 8/23 Introduction, math review, preliminary quiz
• 8/30 The Bayes filter
• 9/6 Labor day, no class
• 9/13 The Bayes filter, the Kalman filter
• 9/20 Quiz 1, Simulation tutorial, Project
  Proposals due
• 9/27 Probabilistic kinematics
• 10/4 Sensor models
• 10/11 Sampling and Particle filtering
• 10/18 Quiz 2
• 10/25 Quiz 2 discussion and papers on
  localization
• 11/1 Mapping
• 11/8 SLAM
• 11/15 Manipulation and grasping
• 11/22 Quiz 3
• 11/29 Final project presentations and demos

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Robotics Yesterday
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Robotics Today
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Robotics Tomorrow?
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What is robotics/a robot ?

• Background
• Term robot invented by Capek in 1921 to mean a
  machine that would willing and ably do our dirty
  work for us
• The first use of robotics as a word appears in
  Asimovs science fiction
• Definition (Brady) Robotics is the intelligent
  connection of perception to action
• History (wikipedia entry is a reasonable intro)

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Contemporary Research Robots

• Cars Stanley_at_Stanford
• Boats and submersibles USC RoboDuck,
  Priceton/MBARI Gliders
• Flying vehicles Stanford Helicopter
• Humanoids Ishiguro Androids

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Trends in Robotics Research

• Classical Robotics (mid-70s)
• exact models
• no sensing necessary

• Hybrids (since 90s)
• model-based at higher levels
• reactive at lower levels

• Probabilistic Robotics (since mid-90s)
• seamless integration of models and sensing
• inaccurate models, inaccurate sensors

Robots are moving away from factory floors to
Entertainment, Toys, Personal service. Medicine,
Surgery, Industrial automation (mining,
harvesting), Hazardous environments (space,
underwater)
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Tasks to be Solved by Robots

• Planning
• Perception
• Modeling
• Localization
• Interaction
• Acting
• Manipulation
• Cooperation
• ...

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Uncertainty is Inherent/Fundamental

• Uncertainty arises from four major factors
• Environment is stochastic, unpredictable
• Robots actions are stochastic
• Sensors are limited and noisy
• Models are inaccurate, incomplete

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Would you like to play a game ?

• Definition (Brady) Robotics is the intelligent
  connection of perception to action

Sensor(s)
Computer
Actuator(s)
The World
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Nature of Sensor Data
Odometry Data
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Probabilistic Robotics

• Key idea Explicit representation of uncertainty
  using the calculus of probability theory
• Perception state estimation
• Action utility optimization

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Advantages and Pitfalls

• Can accommodate inaccurate models
• Can accommodate imperfect sensors
• Robust in real-world applications
• Best known approach to many hard robotics
  problems
• Computationally demanding
• False assumptions
• Approximate