and Course Overview

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Intelligent Robotics: Introduction
and Course Overview





Intelligent Robotics 06
-
13520

Intelligent Robotics (Extended) 06
-
15267

Jeremy Wyatt


School of Computer Science

University of Birmingham, 2005

Plan


Intellectual aims of the module


Task description


Introduction to hardware and software


Using robots to understand intelligence


Module aims


Give an appreciation of the issues that arise when
designing complete, physically embodied autonomous
agents.



Introduce some of the most popular methods for
controlling autonomous mobile robots.


Give hands on experience of engineering design.


Encourage independent thought on possible cognitive
architectures for autonomous agents.


What you will be able to do



Design, build and program simple autonomous robots.





Implement standard signal processing and control algorithms.




Describe and analyse robot processes using appropriate methods.




Write a detailed report on a robot project.




Carry out and write up investigations using appropriate

experimental methods.

Basic Hardware









IR sensors (long, medium,
short)


Whiskers


Microswitches









DC motors


Servo motors


Odometers


Sonar


Additional Hardware


PC104 board + Handyboard


PC104

(Linux)

USB

HB

Sensors

Motors

Camera

USB

Coding on the PC104


Vision routines can be written easily using
extensive libraries from Intel


Multiple processes: threads are wrapped


Download and Run Managers


Support on Handyboard for


pulse counting


new compass


new sonars


smooth pwm

Hardware: a warning


Please take extreme care in handling of all
hardware


no loose connections


tidy soldering


careful charging


check static


if you are not 100% sure then
ASK



We
will not

be able to replace severely broken
kits (you will have to transfer module)

First Week


Handyboard + IC


Aim to build a complete exploring robot by
end of week 2


will familiarise you with sensors and their
properties


will give you practice in robot construction


Base Task Description


Robot Rubbish Clear up


arena with four drop zones


bottles (green)


tennis balls (yellow)


pepsi cans (blue)


coke cans (red)


squiggle ball (any)



collect and correctly deliver the rubbish


2 robots in each bout of 5 mins



Base Task Description


Scoring scheme in assessment handout


Two demos (1 public, 1 private)


Best of two demos


Competition score


influences demo mark


not the only factor


Private demos on the 22n
d
, 24
th

and 25
th
November


Public demo 30
th

November 2
-
4pm


Last Year’s results

Assessment Outline




Assessment by demonstration and report


Report must be


25 pages maximum


10 pt minimum


Hand in 12 noon on


8
th

December




Course Team


Jeremy Wyatt




Lecturer


Noel Welsh




Teaching Assistant


Aleem Hossain, Arjun Chandra



Demonstrators


Ben Stone


system software support


Richard Pannell, Bert Dandy


hardware support

What’s Easy is Hard


Easy: expert systems, mathematics, chess



Hard: seeing, language understanding,
moving around
, making a cup of tea,
common sense



What’s easy for humans is hard for
computers and vice versa. Why?

The Whole Iguana


AI commonly studies aspects of intelligence
separately:



narrow domain high performance




In 1976, philosopher Dan Dennett
suggested putting it all together, but


with a low level of performance

The Whole Iguana

“... why not obtain one's simplicity and scaling down by
attempting to model a whole cognitive creature of much
less sophistication than a human being?... a turtle, perhaps
... [but] considering the abstractness of the problems
properly addressed in AI ... one does not want to bogged
down in the cognitive eccentricities of turtles if the point of
the exercise is to uncover
ver
y general,
very

abstract
properties that will apply as well to the cognitive
organisation of … human beings. So why not make up a
whole cognitive creature, a Martian three
-
wheeled iguana,
say, and an environmental niche for it to cope with? I think
such a project could teach us a great deal about the deep
principles of human cognitive psychology …”

Experiments with vehicles


Behaviour of agents was more complex
than their mechanisms



Behaviour depended on the environment as
well as the agent



Hard to infer mechanism from behaviour
alone


Experiments with vehicles

Valentino Braitenberg
-


“Law of uphill analysis


and downhill invention”


My Conclusion: synthesizing agents may have
something to offer in understanding our
minds

Why build robots to understand
minds?


All naturally occuring intelligence is embodied



So robots are in some ways similar systems



Robots, like animals exploit their environments to
solve specific tasks


“There are no general purpose animals … why
should there be general purpose robots?”







David MacFarland


Lessons from nature


Gannets


wings half open to


control dive



Fold wings to avoid damage



Not at a constant distance, but at


a constant time



Birds have detectors that


calculate time to impact

Task specific robots


Polly the tour guide exploits assumptions
about environment to perform task quickly

Lessons from nature: 2


Other animals are capable of a surprising
degree of manipulative ability e.g.
Betty the
crow
who can
make tools



Sometimes we can use robots to


test theories of how specific


animals work e.g. cricket


phonotaxis


Wrap up


By synthesising intelligent robots we can
address deep questions about the nature of
intelligence



Robots, like animals, are embodied



We can use the task
-
environment dynamics
to constrain our computational problems