1. 159.741 Intelligent Robotics Paper Coordinator: 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

2. Topics for Discussion Pre-requisites Course Overview Learning Outcomes Texts and Course Material Assessment Course Schedule

3. 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:

4. 159.741 Assessment 2 assignments: 40% Seminar + written report + program: 30% The course will be assessed by a combination of practical and theoretical works. CLOSED BOOKThere will be practical works, one seminar and one three hour exam. The exam will be a CLOSED BOOK exam. All assignments will be submitted in class/electronically. Final Exam (3 hours): 30%

5. 159.741 Seminar + 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 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.

6. 159.741 Candidate Research Topics Potential field approach to robot navigation Neuro-Fuzzy approach to robot navigation RESEARCH ASSIGNMENT Complex, specialised robot behaviours Incremental Learning Any hybrid algorithm Any intelligent colour object recognition

7. Input: x, v, theta, angular velocity Control System: Inverted Pendulum Problem 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:

8. Fuzzy Rules Fuzzy 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 right 2.IF cart is on the left AND cart is not moving THEN slightly push cart to the right 3.IF cart is on the left AND cart is going right THEN don’t push cart 4.IF cart is centered AND cart is going left THEN slightly push cart to the right 5.IF cart is centered AND cart is not moving THEN don’t push cart 6.IF cart is centered AND cart is going right THEN slightly push cart to the left 7.IF cart is on the right AND cart is going left THEN don’t push cart 8.IF cart is on the right AND cart is not moving THEN push cart to the left 9.IF cart is on the right AND cart is going right THEN largely push cart to the left

9. Input: x, v, theta, angular velocity Fuzzy Control System Output: 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. X NZEP NPLZE X’ZE P 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.  NZEP NNLNMZE ’’ NMZEPM PZEPMPL

10. Input: MultipleObstacles: x, y, angle Target’s x, y, angle Robot Navigation Output: Robot angle, speed Obstacle Avoidance, Target Pursuit, Opponent Evasion

11. Cascade of Fuzzy Systems Adjusted Speed Adjusted Angle Next Waypoint N Y Adjusted Speed Adjusted Angle Fuzzy 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 behaviours Multiple Fuzzy Systems employ the various robot behaviours Fuzzy System 1 Fuzzy System 2 Fuzzy System 3 Fuzzy System 4 Path Planning Layer Central Control Target Pursuit Obstacle Avoidance

12. Input: Obstacles’ x, y, angle Target’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

13. Simulations 3-D Hybrid Fuzzy A* Navigation System Cascade of Fuzzy Systems

14. Nature as Problem Solver Beauty-of-nature argument How Life Learned to Live (Tributsch, 1982, MIT Press) Example: Nature as structural engineer

15. 15 Genetic Algorithm Let’s see the demonstration for a GA that maximizes the function n =10 c c = 2 30 -1 = 1,073,741,823

16. 16 Simple 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 Fitness Function or Objective Function

17. 17 Test Problem Characteristics 30 With a string length=30, the search space is much larger, and random walk or enumeration should not be so profitable. 2 30 =1.07(10 10 ) points There are 2 30 =1.07(10 10 ) points. With over 1.07 billion points in the space, one- at-a-time methods are unlikely to do very much very quickly. 1.05 0.9 Also, only 1.05 percent of the points have a value greater than 0.9.

18. Page Actual Plot 18 1.050.9 Also, only 1.05 percent of the points have a value greater than 0.9.

19. 19 Simple GA Implementation Initial population of chromosomes Calculate fitness value PopulationOffspring Stop Solution Found? Evolutionary operations Yes No

20. Identifying Colour Objects with

21. Robot Soccer Set-up Colour objects Fluorescent lamps Overhead 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 7 *

22. Machine Vision System 3D Scene Optics (Lens) Image Sensors CameraFrame Grabber 2D Digital Image CCD (Charge Coupled Device) CID (Charge Injection Device) PDA (Photo Diode Array) Firewire camera Emmitted light 2-D Intensity Image Continuous charge signal HARDWARE OUTLINE *

23. Colour 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.

24. Illumination Conditions Colour objects traversing the field under spatially varying illumination intensities We need to automatically compensate for the effects of varying illumination intensities in the scene of traversal * Dark Bright Dim Lens focus Object rotation Quantum electrical effects Shadows Presence of similar colours Other Factors:

25. 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

26. Recent Developments Experiments performed at IIMS Lab 7 PINK colour patches can be amplified to revert back close to its original colour *

27. Robots in action The Fuzzy Vision algorithm employed in the game… Old system Robots at Massey C:\Core\Research\Conferences\ICONIP08