Professor: Marek A. Perkowski, Electrical Engineering.

THE TOPICS TO BE DISCUSSED IN CLASS OR ADDRESSED IN PROJECTS.

  1. INTRODUCTION TO ROBOTICS, MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE.
    1. Read Luger's book.
    2. Read description of our class projects funded by Intel. about our Intel's grant.
    3. Read recent papers and WWW Pages related to hobby electronics and hobby robotics.
    4. Read my WWW pages about Data Mining and Functional Decomposition.
    5. Think why you are interested in robotics, what is your dream project, what you want to learn in this class, what you want to do as your mandatory project. Create a short presentation in PowerPoint about these ideas. Send me by email or give me a diskette in the class. I do not accept other documents.


  2. STANDARD PROJECT NUMBER ONE: DESIGNING SIMPLE ROBOTS USING TOYS AND KITS.

    Project leaders Matt Maple, Michael Kennan.


    The material to be learnt in these projects is the following:
    1. assembling a simple robot from a kit or few kits; 5 - 7 hours at most.
    2. selecting sensors from the catalog, ordering them and adding to the robotic arm; 2 - 5 hours at most.
    3. connecting to a PC through parallel port and learning how to use and adapt the existing software; 2 - 5 hours at most.
    4. writing new robotics software in C, C++, LISP, Prolog or any other language that you know. Time depends on the project complexity; 2 - 5 hours at least.
    5. programming a EPLD, PAL or FPGA with the program of robot's behavior; about 7 hours.


    Currently, we dispose the following kits:
    1. OWI robotic arms.
    2. Robix robotic kits.
    3. LEGO Mindstorms robotic kits.
    4. LEGO add-on kits to Mindstorms.
    5. Capsela robotic kits.
    6. Second-hand electric components (motors, sensors, interfaces) and Erector sets.
    7. Pneumatic hand based on artificial muscles.
    8. Cars and platforms.


    For examples of previous project of this type look to: Projects from summer 1999

  3. STANDARD PROJECT NUMBER TWO: ROBOT FOR MANUFACTURING AND TEST.

    Project leaders Anas Al-Rabadi, Justin Kam, Ron Fehlen, Xiong (Bear).


    These projects use the Rhino robot with connected sensors, assemblies and electronics. The material to be learnt in these projects is the following:
    1. Parallel port and serial port programming.
    2. Simple robotic languages; stand-alone and embedded in Basic and C.
    3. Simple robotic languages embedded in Lisp and Prolog.
    4. Control of movement, equation.
    5. Role of sensors.
    6. Camera: edge detection, noise removal, finding straight lines using Hough Transform.
    7. Test; fault models, test generation, fault location, diagnosis, measurements, bed of nails.
    8. Voice control and voice synthesis.
    9. Simple system integration.


    Variants:
    1. Dice movement control.
    2. Test by a robot.
    3. Fault localization by a robot.
    4. Self-repair of a simple PCB by a robot.
    5. Robot using vision solves the "Man, Wolf, Goat and Cabbage" puzzle wihout human programming.
    6. Robot using vision solves the "Missionaries and Cannibals" puzzle wihout human programming.
    7. Robot using vision solves the "Wolf and 4 Sheep" game wihout human programming.
    8. Robot responds to voice in "Block World", using vision it solves "Tower of Hanoi" or similar problems.
    9. Robot that paints oil painting or spray paintings from examples.
    10. Robot sculptor: Dremel tools, camera, go.


  4. STANDARD PROJECT NUMBER THREE: ROBOT THAT LEARNS.

    Project leaders Michael Levy, Eli Cabelly.


    These projects assembly various human-like or animal-like robots with connected sensors, assemblies and electronics. The material to be learnt in these projects is the following: In all projects many cameras, microphones, sonar sensors, infrared sensors and temperature sensors are used.
    1. Robot makes faces, shows emotions while responding to voice sentences from a human.
    2. Robot responds to voice, recognizes people, mimicques them.


    Variants:
    1. MUVAL: this is an ugly creature with four legs, fat belly and a face from a Halloween mask. It sits on a desk and responds to your commands. This is an ugly creature with four legs, fat belly and a face from a Halloween mask. The name cames from MUltiple-VALued logic, because it reasons using multiple-valued logic. There is currently a competition for the best head of the MUVAL. Three students participate.
    2. GORILLA: the name tells how the robot looks like.
    3. MECHANICAL ROBOT OF THE THIRTIES: looks like from old movies.
    4. THREE BEARS: they talk to you, to one another, and they play music.


    Variants of learning:
    1. Constructive Induction by Decomposition of Multi-Valued relations.
    2. Decomposition of Fuzzy Functions and Relations.
    3. Decision Trees and Diagrams.
    4. Probabilistic Neural Nets.
    5. Rule-Based search with Pattern Recognition used in cost evaluation for rules.


  5. STANDARD PROJECT NUMBER FOUR: MOBILE LEARNING ROBOTS.

    Project leaders none this year.

    1. Navigation. Maps.
    2. Use of controllers.
    3. PSUBOT: A Robotic Wheelchair for Blind Quadruplegic.


  6. PROGRAMMING IN LISP.
    1. Read Perkowski/Mishchenko/Al-Rabadi book.
    2. Read Luger.
    3. Read KCL manuals on line or manuals of your Lisp on PC or Macintosh.


  7. PREDICATE CALCULUS AND RESOLUTION METHOD.
    1. Read Perkowski/Mishchenko/Al-Rabadi book.
    2. Read Luger.
    3. Read Prolog manuals on line or manuals of your Prolog on PC or Macintosh.


  8. STATE SPACE SEARCH.
    1. Read Perkowski/Mishchenko/Al-Rabadi book.
    2. Read Luger.
    3. Read Prolog manuals on line or manuals of your Prolog on PC or Macintosh.


  9. HEURISTIC SEARCH, METHODS FOR REPRESENTATION OF PROBLEMS AND ADVANCED DATA STRUCTURES.
    1. Read Perkowski/Mishchenko/Al-Rabadi book.
    2. Read Luger.


  10. PROGRAMMING IN LOGIC AND CONSTRAINTS
    1. Read Perkowski/Mishchenko/Al-Rabadi book.
    2. Read Luger.
    3. Programming in PROLOG.
    4. Programming in WILD LIFE.
    5. Programming in Constraints.
    6. Reasoning by analogy and induction.


  11. ADVANCED ROBOT PROGRAMMING
    1. Mobile robots ; guidance systems, path planning, collision avoidance.
    2. Robot programming.
    3. Task planning, robot languages.


  12. COMPUTER ARCHITECTURES FOR LOGIC PROGRAMMING AND ARTIFICIAL INTELLIGENCE
    1. Read Perkowski/Mishchenko book.
    2. Cube Calculus Machine.
    3. Eye for the robotic dog.
    4. Analog FPGAs.


  13. IMAGE PROCESSING AND COMPUTER VISION
    1. Introduction to image processing for robotics application.
    2. Introduction to machine learning and computer vision for robotics applications.
    3. Elements of Pattern Recognition.
    4. Sensors. Computer vision hardware.
    5. Low-level image processing; filtering, thinning, edge-detection, region growing.
    6. Medium-level image processing; image transforms, image matching.
    7. Robotic computer vision, use of AI techniques.


  14. DATA MINING AND MACHINE LEARNING
    1. Data Mining and Knowledge Discovery in Data Bases.
    2. Rule-based systems and expert systems.
    3. Fuzzy logic and applications.


  15. APPLICATIONS OF ROBOTIC TECHNOLOGIES
    1. Manufacturing robots.
    2. Military, fire-fighting, police.
    3. Entertainment robots.
    4. Robots in health care and manufacturing.
    5. House-hold robots.