Intelligent Robotics: Introduction and Course Overview

1
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

2
Plan

• Intellectual aims of the module
• Task description
• Introduction to hardware and software
• Using robots to understand intelligence

3
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.

4
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.

5
Basic Hardware

• IR sensors (long, medium, short)
• Whiskers
• Microswitches

• DC motors
• Servo motors
• Odometers
• Sonar

6
Additional Hardware

• PC104 board Handyboard

Sensors
PC104 (Linux)
HB
USB
Motors
USB
Camera
7
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

8
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)

9
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

10
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

11
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 22nd, 24th and 25th November
• Public demo 30th November 2-4pm
• Last Years results

12
Assessment Outline

• Assessment by demonstration and report
• Report must be
• 25 pages maximum
• 10 pt minimum
• Hand in 12 noon on
• 8th December

13
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

14
Whats Easy is Hard

• Easy expert systems, mathematics, chess
• Hard seeing, language understanding, moving
  around, making a cup of tea, common sense
• Whats easy for humans is hard for computers and
  vice versa. Why?

15
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

16
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 very
  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

17
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

18
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

19
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

20
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

21
Task specific robots

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

22
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

23
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