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Introduction to Vision & Robotics. Bob Fisher 651-3441 [email protected] IF 1.26 Michael Herrmann IF 1.42 [email protected] 651-7177. Lectures: Handouts (+ video) will be on the web (but are not a substitute for lecture attendance) Practicals : please sign up for a time-slot (AT 3.01)

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Introduction to vision robotics
Introduction to Vision & Robotics

Lectures:

Handouts(+ video) will be on the web (but are not a substitute for lecture attendance)

Practicals: please sign up for a time-slot (AT 3.01)

(Practicals start week 2)

Problems: let MH or RBF know or see class reps!


Overview of the course

Exam

75%

Overview of the course:

Assessment

  • Lectures:

    • Sensing and Vision

    • Effectors and Control

    • Architectures and wider issues

  • Practicals:

    • Pract 1: webcam visual tracking of ‘robot’

    • Pract 2: webot based servo control

25%



Renaissance optics:

The algorithmic connection between the world and the image - Durer c.1500


Watt & Boulton 1788

Feedback

Control

First Control

Theory


18th century clockwork animals

Vaucanson’s duck


Early 20th century

Electronic devices for remote control – Tesla

Methods for transducing images into electrical signals

‘Robot’ used to describe artificial humanoid slaves in Capek’s play “Rossum’s Universal Robots” 1920


1940s –1950s

Development of electronic computer and control theory

Used for artificial creatures e.g. Walter’s ‘tortoise’ and John Hopkins’ ‘beast’


1960s

Industrial robot arms:

Unimation

Methods for image enhancement and pattern recognition


1970s

Work on systems in restricted domains

e.g. Shakey in blocks world

Freddy assembly task


1980s

Tackling more realistic problems:

Natural scene analysis

Face recognition

Dynamic locomotion

Significant impact in manufacturing

Active vision


Vision and robotics use much of ai
Vision and robotics use much of AI:

  • Problem solving, planning, search, inference, knowledge representation, learning etc...

  • Have constraints such as:

    • Limited, noisy, raw information

    • Continuous dynamic problem space

    • Time, power, cost and hardware limitations

  • Often solutions grounded in these constraints do not resemble conventional AI approaches


A challenging problem
A challenging problem

  • Building vision and robot systems involves a variety of interacting technology domains:

    • Mechanical, electrical, digital, computational...

  • This has proved to be a hard problem for AI

    • Can beat the human grandmaster at chess

    • Can't replace a house cleaner


Applications dull dirty or dangerous
Applications: dull, dirty or dangerous

Visual inspection of parts

Detecting crime on CCTV

Welding on cars

N.B. Overlap with automation


Applications dull dirty or dangerous1
Applications: dull, dirty or dangerous

Robot vacuum cleaners

Cleaning nuclear plants

Robot sewer inspection

N.B. Overlaps with teleoperation


Applications dull dirty or dangerous2
Applications: dull, dirty or dangerous

Visual aids for driving

Demining

Space exploration


Applications also
Applications: also...?

Entertainment industry

Service industry

Science


Some Interesting Robots


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