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 - - 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
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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Robot s
- Manipulat ors
– Physically anchored – Most indust r ial robot s
- Assembly lines
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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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Shakey t he robot
- First mobile robot
(SRI 1968)
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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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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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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