159.741

Intelligent Robotics

Paper Coordinator: Paper Coordinator:

Dr. Napoleon H. Reyes, Ph.D.Dr. Napoleon H. Reyes, Ph.D.

Computer Science

Institute of Information and Mathematical Sciences

Rm. 2.56 QA, or IIMS Lab 7, Albany Campus

email: n.h.reyes@massey.ac.nz

Tel. No.: 64 9 4140800 x 9512 or 41572

Fax No.: 64 9 441 8181

159.741

159.741

Topics for Discussion

Pre-requisites

Course Overview

Learning Outcomes

Texts and Course Material

Assessment

Course Schedule

159.741

Design and implement algorithms for control, classification and optimization systems.

Learning Outcomes

Describe the main algorithms used in building intelligent systems..

Identify the advantages and disadvantages of applying various AI techniques in solving real world problems.

On successful completion of the course, the students should be able to:

159.741

Assessment

2 assignments: 40%

Seminar + written report + program: 30%

• The course will be assessed by a combination of practical and theoretical works.

• There will be practical works, one seminar and one three hour exam. The exam will be a CLOSED BOOKCLOSED BOOK exam.

• All assignments will be submitted in class/electronically.

Final Exam (3 hours): 30%

159.741Seminar + report + codeSeminar + report + code

A research topic will have to be proposed. Upon my approval, you can use it for your seminar.

The seminar is to be presented in class (20-25 minutes)

RESEARCH ASSIGNMENTRESEARCH ASSIGNMENT

The report should discuss the theory and algorithms well.

All formulas should be explained, and there should be an accompanying sample computation for each.

A sample code simulating the algorithm must be submitted. Instructions on how to use the code must be included in the documentation.

159.741Candidate Research TopicsCandidate Research Topics

Potential field approach to robot navigation

Neuro-Fuzzy approach to robot navigation

RESEARCH ASSIGNMENTRESEARCH ASSIGNMENT

Complex, specialised robot behaviours

Incremental Learning

Any hybrid algorithm

Any intelligent colour object recognition

Input: x, v, theta, angular velocity

Control System: Inverted Pendulum Control System: Inverted Pendulum ProblemProblem

Control System: Inverted Pendulum Control System: Inverted Pendulum ProblemProblem

Output: Force, direction

Otherwise known as Broom-Balancing Problem

The mathematical solution uses a second-order differential equation that describes cart motion as a function of pole position and velocity:

sinsin)cos(cos)sin(2

2

2

2

mglllt

mllxt

m

Fuzzy RulesFuzzy rule base and the corresponding FAMM for the velocity and position vectors of the inverted pendulum-balancing problem

1. IF cart is on the left AND cart is going left THEN largely push cart to the right2. IF cart is on the left AND cart is not moving THEN slightly push cart to the right3. IF cart is on the left AND cart is going right THEN don’t push cart4. IF cart is centered AND cart is going left THEN slightly push cart to the right5. IF cart is centered AND cart is not moving THEN don’t push cart6. IF cart is centered AND cart is going right THEN slightly push cart to the left7. IF cart is on the right AND cart is going left THEN don’t push cart8. IF cart is on the right AND cart is not moving THEN push cart to the left9. IF cart is on the right AND cart is going right THEN largely push cart to the left

Input: x, v, theta, angular velocityInput: x, v, theta, angular velocity

Fuzzy Control System

Output: Force, directionOutput: Force, direction

Inverted Pendulum Problem

If the cart is too near the end of the path, then regardless of the state of the broom angle push the cart towards the other end.

If the cart is too near the end of the path, then regardless of the state of the broom angle push the cart towards the other end.

X 

N ZE P

N PL ZE ZE

X’ ZE ZE ZE ZE

P ZE ZE NL

If the broom angle is too big or changing too quickly, then regardless of the location of the cart on the cart path, push the cart towards the direction it is leaning to.

If the broom angle is too big or changing too quickly, then regardless of the location of the cart on the cart path, push the cart towards the direction it is leaning to.

 

N ZE P

N NL NM

ZE

’ ZE NM

ZE PM

P ZE PM

PL

Input: Multiple Obstacles: x, y, angleTarget’s x, y, angle

Robot Navigation

Output: Robot angle, speed

Obstacle Avoidance, Target Pursuit, Opponent Evasion

Cascade of Fuzzy SystemsCascade of Fuzzy Systems

Adjusted Speed

Adjusted Angle

Next Waypoint

N

Y

Adjusted Speed

Adjusted Angle

Fuzzy System 1: Target PursuitFuzzy System 1: Target Pursuit

Fuzzy System 2: Speed Control for Target Pursuit

Fuzzy System 3: Obstacle Avoidance

Fuzzy System 4: Speed Control for Obstacle Avoidance

ObstacleDistance < MaxDistanceTolerance and closer than Target

Actuators

Path planning Layer:

The A* Algorithm

Multiple Fuzzy Systems employ the various robot behavioursMultiple Fuzzy Systems employ the various robot behaviours

Fuzzy System 1Fuzzy System 1

Fuzzy System 2Fuzzy System 2

Fuzzy System 3Fuzzy System 3

Fuzzy System 4Fuzzy System 4

Path Planning LayerPath Planning Layer

CentralControl

Target Target PursuitPursuit

ObstacleObstacleAvoidanceAvoidance

Input: Obstacles’ x, y, angleTarget’s x, y, angle

Hybrid Fuzzy A*

Output: Robot angle, speed

C:\Core\Massey Papers\159302\Assignments 2008\Assign #2 - 2008\Robot Navigation - v.9.4 - FL-AStar

Simulations

3-D Hybrid Fuzzy A* Navigation System3-D Hybrid Fuzzy A* Navigation System

Cascade of Fuzzy SystemsCascade of Fuzzy Systems

Nature as Problem Solver

• Beauty-of-nature argument

• How Life Learned to Live (Tributsch, 1982, MIT Press)

• Example: Nature as structural engineer

15

Genetic Algorithm

• Let’s see the demonstration for a GA that maximizes the function

n

c

xxf

)(

n =10cc = 230 -1 = 1,073,741,823

16

Simple GA ExampleSimple GA Example• Function to evaluate:

• coeff – chosen to normalize the x parameter when a bit string of length lchrom =30 is chosen.

• Since the x value has been normalized, the max. value of the function will be:

when for the case when lchrom=30

10

( )x

f xcoeff

302 1coeff

( ) 1.0f x

302 1x

Fitness Function or Objective

Function

17

Test Problem CharacteristicsTest Problem Characteristics

• With a string length=3030, the search space is much larger, and random walk or enumeration should not be so profitable.

• There are 223030=1.07(10=1.07(101010) points) points. With over 1.07 billion points in the space, one-at-a-time methods are unlikely to do very much very quickly. Also, only 1.051.05 percent of the points have a value greater than 0.90.9.

Page

Actual PlotActual Plot

18

Also, only 1.051.05 percent of the points have a value greater than 0.90.9.

19

Simple GA ImplementationInitial population of chromosomes

Calculate fitness value

PopulationOffspring

Stop

SolutionFound?

Evolutionaryoperations

Yes

No

Identifying Colour ObjectsIdentifying Colour Objectswitwithh

Robot Soccer Set-upRobot Soccer Set-up

Colour objects

Fluorescent lampsOverhead Camera

Exploratory environment is indoor – room totally obstructed from sunlight

Multiple monochromatic light sources – fluorescent / fluoride lamps

Colour Object Recognition (Recognition speed: < 33ms)

www.Fira.net

IIMS Lab 7IIMS Lab 7

**

Machine Vision SystemMachine Vision System

3D Scene

Optics (Lens)

Image Sensors

Camera Frame Grabber 2D Digital Image

CCD (Charge Coupled Device)CID (Charge Injection Device)

PDA (Photo Diode Array)

Firewire camera

Emmitted light2-D Intensity ImageContinuous charge signal

HARDWARE OUTLINE

**

Colour as the machine sees itColour as the machine sees it

Colour constancy is inherent in us humans, but not in cameras.Colour constancy is inherent in us humans, but not in cameras.

Color is not captured by the camera as we humans see it.

Yellow object turns pale under strong white illumination

A Green object tends to appear more as a whitish yellow object under bright white illumination.

Illumination ConditionsIllumination Conditions

Colour objects traversing the field under spatially varying Colour objects traversing the field under spatially varying illumination intensitiesillumination intensities

We need to automatically compensate for theWe need to automatically compensate for theeffects of varying illumination intensities in effects of varying illumination intensities in the scene of traversalthe scene of traversal

**

Dark

Bright

Dim

Lens focusLens focus

Object rotationObject rotation

Quantum electrical effectsQuantum electrical effects

ShadowsShadows

Presence of similar coloursPresence of similar colours

Other Factors:Other Factors:

Recent Developments

To some extent, the algorithm can see in the dark

Applying the colour contrast operations to compensate for the effects of glare, hue and saturation drifting also allows for colour correction

Experiments performed at IIMS Lab 7

Recent Developments

Experiments performed at IIMS Lab 7

PINK colour patches can be amplified to revert back close to its original colour

**

Robots in action

The Fuzzy Vision algorithm employed in the game…

Old system

Robots at Massey

C:\Core\Research\Conferences\ICONIP08