Lecture 18 Robots Introduction - Ohio University

jabgoldfishΤεχνίτη Νοημοσύνη και Ρομποτική

19 Οκτ 2013 (πριν από 3 χρόνια και 9 μήνες)

76 εμφανίσεις

Robots

Introduction

Based on the lecture by

Dr.
Hadi Moradi

University of Southern California


Outline


Control Approaches


Feedback Control


Cybernetics


Braitenberg Vehicles


Artificial Intelligence


Early robots


Robotics Today


Why is Robotics hard

Control


Sensing
=>

Action


Reactive


Don’t think, act: Animals


Deliberative


Think hard, act later: Chess


Hybrid


Think and act in parallel: car races


Behavior
-
based


Think the way you act: human

Reactive Systems



Collection of sense
-
act rules


Stimulus
-
response


Advantages:


?


Disadvantages


?


Reactive Systems



Collection of sense
-
act rules


Stimulus
-
response


Advantages:


Inherently parallel


No/minimal state


Very fast


No memory


Disadvantages


No planning


No learning


Deliberative Systems


3 phase model:


Sense


Plan


Act


Example: Chess


Advantages:


?


Disadvantages:


?

Deliberative Systems


3 phase model:


Sense


Plan


Act


Advantages:


can plan


Can learn


Disadvantages:


Needs world model


Searching and planning are slow


World model gets outdated

Feedback Control


React to the sensor changes


Feedback control == self
-
regulation


Q: What type of control system is it?



Feedback types:


Positive


Negative

-

and + Feedback


Negative feedback:


Regulates the state/output


Examples: Thermostat, bodies, …


Positive feedback:


Amplifies the state/output


Examples: Stock market


The first use: ancient Greek water system


Re
-
invented in the Renaissance for ovens

W. Grey Walter’s Tortoise


1953


Machina Speculatrix


Sensors


1 photocell,


1 bump sensor


2 motors


Reactive control


W. Grey Walter’s Tortoise


Behaviors:


seeking light,


head toward weak light,



back away from bright
light,


turn and push (obstacle
avoidance),


recharge battery.


Basis for creating adaptive
behavior
-
based

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


productive or unproductive behavior,
adaptation

Ducking

Tortoise behavior


A path: a
candle on top
of the shell

Tortoise behavior


Two turtles: Like dancing

New Tortoise

Question


How does it do the
charging?


Note: When the
battery is low, it goes
for the light.

Braitenberg Vehicles


Valentino Braitenberg


early 1980s


Extended Walter’s mode


Based on analog circuits


Direct connections between
light sensors and motors


Complex behaviors from very
simple mechanisms

Braitenberg Vehicles


Complex behaviors from very simple
mechanisms

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


Check:
http://www.duke.edu/~mrz/braitenberg/braitenberg.html


B
ot
s

order Styrofoam cubes

(16 min 30 sec)


Tokyo Lecture 3 time 24:30
-
41:00



Brief History


1750: Swiss craftsman create automatons with
clockwork to play tunes


1917: Word Robot appeard in Karel Capek’s play


1938: Issac Asimov wrote a novel about robots


1958: Unimation (Universal Automation) co started
making die
-
casting robots for GM


1960: Machine vision studies started


1966: First painting robot installed in Byrne, Norway.


1966: U.S.A.’s robotic spacecraft lands on moon.


1978: First PUMA (Programmable Universal Assembly)
robot developed by Unimation.


1979: Japan introduces the SCARA (Selective
Compliance Assembly Robot Arm).

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


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...


Early robots used deliberative control


Intelligence through construction (5 min 20 sec)


Tokyo Lecture 2 time 27:40
-
33:00