452 lecture9

Information about 452 lecture9

Published on May 2, 2008

Author: Maitane

Source: authorstream.com

Content

Introduction to mobile robots -2:  Introduction to mobile robots -2 Slides modified from Maja Mataric’s CSCI445, USC Last time we saw::  Last time we saw: Defining “robot” What makes a robot Sensors, sensor space State, state space Action/behavior, effectors, action space The spectrum of control Reactive systems Lecture Outline:  Lecture Outline More on the spectrum of control Deliberative and hybrid control A brief history of robotics Feedback control Cybernetics Artificial Intelligence (AI) Early robotics Robotics today Why is robotics hard? Control:  Control Robot control refers to the way in which the sensing and action of a robot are coordinated. The many different ways in which robots can be controlled all fall along a well-defined spectrum of control. Control Approaches:  Control Approaches Reactive Control Don’t think, (re)act. Deliberative Control Think hard, act later. Hybrid Control Think and act independently, in parallel. Behavior-Based Control Think the way you act. Reactive Systems:  Reactive Systems Collections of sense-act (stimulus-response) rules Inherently concurrent (parallel) No/minimal state No memory Very fast and reactive Unable to plan ahead Unable to learn Deliberative Systems:  Deliberative Systems Based on the sense->plan->act (SPA) model Inherently sequential Planning requires search, which is slow Search requires a world model World models become outdated Search and planning takes too long Hybrid Systems:  Hybrid Systems Combine the two extremes reactive system on the bottom deliberative system on the top connected by some intermediate layer Often called 3-layer systems Layers must operate concurrently Different representations and time-scales between the layers The best or worst of both worlds? Behavior-Based Systems:  Behavior-Based Systems An alternative to hybrid systems Have the same capabilities the ability to act reactively the ability to act deliberatively There is no intermediate layer A unified, consistent representation is used in the whole system=> concurrent behaviors That resolves issues of time-scale A Brief History:  A Brief History Feedback control Cybernetics Artificial Intelligence Early Robotics Feedback Control:  Feedback Control Feedback: continuous monitoring of the sensors and reacting to their changes. Feedback control = self-regulation Two kinds of feedback: Positive Negative The basis of control theory - and + Feedback:  - and + Feedback Negative feedback acts to regulate the state/output of the system e.g., if too high, turn down, if too low, turn up thermostats, toilets, bodies, robots... Positive feedback acts to amplify the state/output of the system e.g., the more there is, the more is added lynch mobs, stock market, ant trails... Uses of Feedback:  Uses of Feedback Invention of feedback as the first simple robotics (does it work with our definition)? The first example came from ancient Greek water systems (toilets) Forgotten and re-invented in the Renaissance for ovens/furnaces Really made a splash in Watt's steam engine Cybernetics:  Cybernetics Pioneered by Norbert Wiener (1940s) (From Greek “steersman” of steam engine) Marriage of control theory (feedback control), information science and biology Seeks principles common to animals and machines, especially for control and communication Coupling an organism and its environment (situatedness) W. Grey Walter’s Tortoise:  W. Grey Walter’s Tortoise Machina Speculatrix 1 photocell & 1 bump sensor, 1 motor Behaviors: seek light head to weak light back from bright light turn and push recharge battery Reactive control Turtle Principles:  Turtle Principles Parsimony: simple is better (e.g., clever recharging strategy) Exploration/speculation: keeps moving (except when charging) Attraction (positive tropism): motivation to approach light Aversion (negative tropism): motivation to avoid obstacles, slopes Discernment: ability to distinguish and make choices, i.e., to adapt The Walter Turtle in Action:  The Walter Turtle in Action Braitenberg Vehicles:  Braitenberg Vehicles Valentino Braitenberg (early 1980s) Extended Walter’s model in a series of thought experiments Also based on analog circuits Direct connections (excitatory or inhibitory) between light sensors and motors Complex behaviors from simple very mechanisms Braitenberg Vehicles:  Braitenberg Vehicles Examples of Vehicles: V1: V2: http://people.cs.uchicago.edu/~wiseman/vehicles/ Braitenberg Vehicles:  Braitenberg Vehicles By varying the connections and their strengths, numerous behaviors result, e.g.: “fear/cowardice” - flees light “aggression” - charges into light “love” - following/hugging many others, up to memory and learning! Reactive control Later implemented on real robots Early Artificial Intelligence:  Early Artificial Intelligence “Born” in 1955 at Dartmouth “Intelligent machine” would use internal models to search for solutions and then try them out (M. Minsky) => deliberative model! Planning became the tradition Explicit symbolic representations Hierarchical system organization Sequential execution Artificial Intelligence (AI):  Artificial Intelligence (AI) Early AI had a strong impact on early robotics Focused on knowledge, internal models, and reasoning/planning Eventually (1980s) robotics developed more appropriate approaches => behavior-based and hybrid control AI itself has also evolved... But before that, early robots used deliberative control Early Robots: SHAKEY:  Early Robots: SHAKEY At Stanford Research Institute (late 1960s) Vision and contact sensors STRIPS planner Visual navigation in a special world Deliberative Early Robots: HILARE:  Early Robots: HILARE LAAS in Toulouse, France (late 1970s) Video, ultrasound, laser range-finder Still in use! Multi-level spatial representations Deliberative -> Hybrid Control Early Robots: CART/Rover:  Early Robots: CART/Rover Hans Moravec Stanford Cart (1977) followed by CMU rover (1983) Sonar and vision Deliberative control Robotics Today:  Robotics Today Assembly and manufacturing (most numbers of robots, least autonomous) Materials handling Gophers (hospitals, security guards) Hazardous environments (Chernobyl) Remote environments (Pathfinder) Surgery (brain, hips) Tele-presence and virtual reality Entertainment Why is Robotics hard?:  Why is Robotics hard? Sensors are limited and crude Effectors are limited and crude State (internal and external, but mostly external) is partially-observable Environment is dynamic (changing over time) Environment is full of potentially-useful information Key Issues:  Key Issues Grounding in reality: not just planning in an abstract world Situatedness (ecological dynamics): tight connection with the environment Embodiment: having a body Emergent behavior: interaction with the environment Scalability: increasing task and environment complexity

Related presentations


Other presentations created by Maitane

Introduction DFT
16. 10. 2007
0 views

Introduction DFT

ch04 org cell
08. 05. 2008
0 views

ch04 org cell

taylor forcefeedback
08. 05. 2008
0 views

taylor forcefeedback

lecture01
07. 05. 2008
0 views

lecture01

majumdar iccs05
02. 05. 2008
0 views

majumdar iccs05

accident causation
02. 05. 2008
0 views

accident causation

JohnAdamCAS
02. 05. 2008
0 views

JohnAdamCAS

v1exploratorium
02. 05. 2008
0 views

v1exploratorium

Ode to a rat
03. 10. 2007
0 views

Ode to a rat

crocodile physics
11. 10. 2007
0 views

crocodile physics

Critical Thinking
12. 10. 2007
0 views

Critical Thinking

Lectures 2
16. 10. 2007
0 views

Lectures 2

USLA
22. 10. 2007
0 views

USLA

Typologie etudiants 2005 2006
24. 10. 2007
0 views

Typologie etudiants 2005 2006

Land Ho The Isthmus Forms
25. 10. 2007
0 views

Land Ho The Isthmus Forms

Nobelpreis 2004
15. 10. 2007
0 views

Nobelpreis 2004

city sadness
01. 11. 2007
0 views

city sadness

ast110 02
13. 11. 2007
0 views

ast110 02

Mura
15. 10. 2007
0 views

Mura

INECOR Amar RACHEDI
24. 10. 2007
0 views

INECOR Amar RACHEDI

Intro RED Presentation
24. 10. 2007
0 views

Intro RED Presentation

MarseilleImprimable
24. 10. 2007
0 views

MarseilleImprimable

EOT Presentation
02. 11. 2007
0 views

EOT Presentation

OM 2005 11
16. 11. 2007
0 views

OM 2005 11

csa2070 Formal slides
16. 11. 2007
0 views

csa2070 Formal slides

AdCooperate
11. 10. 2007
0 views

AdCooperate

CAPTIC2003 SV
23. 10. 2007
0 views

CAPTIC2003 SV

HPS PDS Yu 7 15 2007
01. 01. 2008
0 views

HPS PDS Yu 7 15 2007

f06 wk10
04. 01. 2008
0 views

f06 wk10

69A Job Safety Analysis Bayne
07. 01. 2008
0 views

69A Job Safety Analysis Bayne

Public Procurement Oct04 Notes
07. 01. 2008
0 views

Public Procurement Oct04 Notes

IST444Genomicsequenc ing
16. 10. 2007
0 views

IST444Genomicsequenc ing

informesept2006
22. 10. 2007
0 views

informesept2006

FunDgnCaseStudies2005
04. 10. 2007
0 views

FunDgnCaseStudies2005

SLP PP
22. 10. 2007
0 views

SLP PP

BinghamEtalWAFINAL
04. 12. 2007
0 views

BinghamEtalWAFINAL

ecoI 5
15. 10. 2007
0 views

ecoI 5

LIDAR Leblanc
19. 10. 2007
0 views

LIDAR Leblanc

orlando opening
29. 10. 2007
0 views

orlando opening

limestone
06. 12. 2007
0 views

limestone

212w
05. 10. 2007
0 views

212w

FPBASPSreport2006
04. 10. 2007
0 views

FPBASPSreport2006

PowerIndTrucksslides
27. 02. 2008
0 views

PowerIndTrucksslides

The Six Nutrients
04. 03. 2008
0 views

The Six Nutrients

fall trash
29. 02. 2008
0 views

fall trash

icfa korea may05
13. 03. 2008
0 views

icfa korea may05

traveling
03. 04. 2008
0 views

traveling

economic cycle
08. 04. 2008
0 views

economic cycle

LAH FADR 2007 01
21. 10. 2007
0 views

LAH FADR 2007 01

retrospective10year
09. 04. 2008
0 views

retrospective10year

telemonitor full
17. 04. 2008
0 views

telemonitor full

rules
22. 04. 2008
0 views

rules

History 101
23. 10. 2007
0 views

History 101

Agrofuels DrivingClimateChange
07. 04. 2008
0 views

Agrofuels DrivingClimateChange

cdc intervention
31. 10. 2007
0 views

cdc intervention

proj 05
28. 02. 2008
0 views

proj 05

appascom
01. 10. 2007
0 views

appascom

Ross7eCh11
16. 04. 2008
0 views

Ross7eCh11

Sato pres 06
19. 02. 2008
0 views

Sato pres 06

cvamia 2004
20. 02. 2008
0 views

cvamia 2004

QDS
20. 11. 2007
0 views

QDS

poster aises
26. 11. 2007
0 views

poster aises

CM1 NY Swipe Card presentation
28. 09. 2007
0 views

CM1 NY Swipe Card presentation

mississauga
05. 10. 2007
0 views

mississauga

RuralDevSeminar
18. 10. 2007
0 views

RuralDevSeminar

slides prog 65
30. 10. 2007
0 views

slides prog 65

SachsFelix
26. 02. 2008
0 views

SachsFelix

PSC300Week7Tue
23. 12. 2007
0 views

PSC300Week7Tue

Claus xpforedrag
06. 11. 2007
0 views

Claus xpforedrag

pre as18jan48
25. 03. 2008
0 views

pre as18jan48

RA2 conclusions 2
14. 02. 2008
0 views

RA2 conclusions 2