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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: [email protected]
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