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Introduction to Vision & Robotics

Introduction to Vision & Robotics. Bob Fisher 651-3441 rbf@inf.ed.ac.uk IF 1.26 Michael Herrmann IF 1.42 mherrman@inf 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

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  1. Introduction to Vision & Robotics • Bob Fisher 651-3441 • rbf@inf.ed.ac.uk IF 1.26 • Michael Herrmann IF 1.42 • mherrman@inf 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) (Practicals start week 2) Problems: let MH or RBF know or see class reps!

  2. 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%

  3. Ancient Greek hydraulic and mechanical automata Hero of Alexandria AD 100

  4. Renaissance optics: The algorithmic connection between the world and the image - Durer c.1500

  5. Watt & Boulton 1788 Feedback Control First Control Theory

  6. 18th century clockwork animals Vaucanson’s duck

  7. 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

  8. 1940s –1950s Development of electronic computer and control theory Used for artificial creatures e.g. Walter’s ‘tortoise’ and John Hopkins’ ‘beast’

  9. 1960s Industrial robot arms: Unimation Methods for image enhancement and pattern recognition

  10. 1970s Work on systems in restricted domains e.g. Shakey in blocks world Freddy assembly task

  11. 1980s Tackling more realistic problems: Natural scene analysis Face recognition Dynamic locomotion Significant impact in manufacturing Active vision

  12. 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

  13. 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

  14. Applications: dull, dirty or dangerous Visual inspection of parts Detecting crime on CCTV Welding on cars N.B. Overlap with automation

  15. Applications: dull, dirty or dangerous Robot vacuum cleaners Cleaning nuclear plants Robot sewer inspection N.B. Overlaps with teleoperation

  16. Applications: dull, dirty or dangerous Visual aids for driving Demining Space exploration

  17. Applications: also...? Entertainment industry Service industry Science

  18. Some Interesting Robots

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