Robot ics J uly 26, 2005 CS 486/ 686 Universit y of Wat erloo - - PowerPoint PPT Presentation

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Robot ics J uly 26, 2005 CS 486/ 686 Universit y of Wat erloo - - PowerPoint PPT Presentation

Robot ics J uly 26, 2005 CS 486/ 686 Universit y of Wat erloo Out line Robot ics Percept ion Planning Reading: R&N Sect . 25.1-25.4 2 CS486/686 Lecture Slides (c) 2005 P. Poupart Robot s Manipulat ors


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Robot ics

J uly 26, 2005 CS 486/ 686 Universit y of Wat erloo

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CS486/686 Lecture Slides (c) 2005 P. Poupart

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Out line

  • Robot ics

– Percept ion – Planning

  • Reading: R&N Sect . 25.1-25.4
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CS486/686 Lecture Slides (c) 2005 P. Poupart

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Robot s

  • Manipulat ors

– Physically anchored – Most indust r ial robot s

  • Assembly lines
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CS486/686 Lecture Slides (c) 2005 P. Poupart

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Robot s

  • Mobile r obot s

– Shakey t he robot (SRI 1968)

  • First mobile robot

– Service robot s (CMU’s Minerva)

  • Museum t our guide robot

– Unmanned land vehicle (NavLab)

  • Aut onomous highway driving

– Unmanned air vehicles

  • Surveillance, crop-spraying, milit ary operat ions

– Aut onomous underwat er vehicles

  • Deep see explorat ion

– Planet ary rovers (Nasa’s Soj ourner)

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CS486/686 Lecture Slides (c) 2005 P. Poupart

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Shakey t he robot

  • First mobile robot

(SRI 1968)

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CS486/686 Lecture Slides (c) 2005 P. Poupart

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Nasa’s Soj ourner

  • Planet ary

rover

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Honda’s P3 and Asimo

  • Humanoid

robot s

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CS486/686 Lecture Slides (c) 2005 P. Poupart

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Robot Hardware

  • Sensors

– Range f inder s (sonar s, laser s) – Tact ile sensors – GPS – I maging (video cameras) – Propriocept ive sensors (odomet ry) – Microphones

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Range scan example

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Robot ic Percept ion

  • Challenge: noisy sensors
  • What st at ist ical model should we use t o

inf er t he st at e of t he world?

  • HMMs (or DBNs)

s0 s1 s2 s3 s4

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Robot Localisat ion

  • Sebast ian Thrun
  • ht t p:/ / robot s.st anf ord.edu/ movies/ sca80a0.avi
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Simult aneous mapping and localisat ion

  • ht t p:/ / robot s.st anf ord.edu/ movies/ mapping1-new.avi
  • Sebast ian Thrun
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Robot Hardware

  • Ef f ect ors

– Revolut e j oint s and prismat ic j oint s – Gripper s – Wheels – Legs – Speaker s

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Degrees of Freedom

  • A robot has one degree of f reedom f or

each independent direct ion of movement

R R R P R R

6 degrees of freedom

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CS486/686 Lecture Slides (c) 2005 P. Poupart

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Degrees of Freedom

  • How many degrees of f reedom (DOF)

does a car have?

– 3 ef f ect ive DOF: x, y, orient at ion – 2 cont rollable DOF

θ

(x,y)

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Degrees of Freedom

  • Holonomic robot s:

– # ef f ect ive DOF = # cont rollable DOF – Most robot arms – Easy t o cont rol – Complex mechanics

  • Non-holonomic robot s

– # ef f ect ive DOF > # cont rollable DOF – Most mobile robot s – Harder t o cont rol – Simple mechanics

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Planning

  • Challenge:

– Noisy sensors – Uncert ain act ion ef f ect s

  • What st at ist ical model can we use?

– Par t ially observable Markov decision process (POMDP) – Dynamic decision net work (DDN)

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POMDP (or DDN)

  • Graphical Represent at ion

s0 s1 s2 s3 s4 a0 a1 a2 a3 r1 r2 r3 r4

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Greedy Pat ient Finding

  • Sebast ian Thrun
  • ht t p:/ / robot s.st anf ord.edu/ movies/ bad.people.avi
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POMDP Pat ient Finding

  • Sebast ian Thrun
  • ht t p:/ / robot s.st anf ord.edu/ movies/ good.people.avi
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Ot her subf ields of Robot ics

  • Mechanics

– Hardware engineering

  • Cont rol

– Form of planning – Mainly concerned wit h “st abilit y”

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Next Class

  • Next Class:
  • Course wrap up
  • Final exam inf o
  • Ot her AI courses