Intelligent Robotics

1. 159.741 Paper Coordinator: Dr. Napoleon H. Reyes, Ph.D. Intelligent Robotics 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

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

3. On successful completion of the course, the students should be able to: Learning Outcomes Describe the main algorithms used in building intelligent systems.. Design and implement algorithms for control, classification and optimization systems. Identify the advantages and disadvantages of applying various AI techniques in solving real world problems.

4. 2 assignments: 40% Seminar + written report + program: 30% Assessment Final Exam (3 hours): 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 BOOK exam. • All assignments will be submitted in class/electronically.

5. RESEARCH ASSIGNMENT A research topic will have to be proposed. Upon my approval, you can use it for your seminar. Seminar + report + code The seminar is to be presented in class (20-25 minutes) 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. RESEARCH ASSIGNMENT Potential field approach to robot navigation Candidate Research Topics Neuro-Fuzzy approach to robot navigation Complex, specialised robot behaviours Incremental Learning Any hybrid algorithm Any intelligent colour object recognition

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

8. Fuzzy Rules Fuzzy rule base and the corresponding FAMM for the velocity and position vectors of the inverted pendulum-balancing problem • IF cart is on the left AND cart is going left THEN largely push cart to the right • IF cart is on the left AND cart is not moving THEN slightly push cart to the right • IF cart is on the left AND cart is going right THEN don’t push cart • IF cart is centered AND cart is going left THEN slightly push cart to the right • IF cart is centered AND cart is not moving THEN don’t push cart • IF cart is centered AND cart is going right THEN slightly push cart to the left • IF cart is on the right AND cart is going left THEN don’t push cart • IF cart is on the right AND cart is not moving THEN push cart to the left • IF cart is on the right AND cart is going right THEN largely push cart to the left

9. Fuzzy Control System Inverted Pendulum Problem 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 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. Input: x, v, theta, angular velocity Output: Force, direction

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

11. Cascade of Fuzzy Systems Multiple Fuzzy Systems employ the various robot behaviours Path planning Layer: The A* Algorithm Path Planning Layer Next Waypoint Fuzzy System 1 Fuzzy System 1:Target Pursuit Target Pursuit Adjusted Angle Central Control Fuzzy System 2: Speed Control for Target Pursuit Fuzzy System 2 Adjusted Speed ObstacleDistance < MaxDistanceTolerance and closer than Target N Y Fuzzy System 3: Obstacle Avoidance Fuzzy System 3 Obstacle Avoidance Adjusted Angle Fuzzy System 4 Fuzzy System 4: Speed Control for Obstacle Avoidance Adjusted Speed Actuators

12. Hybrid Fuzzy A* Input: Obstacles’ x, y, angle Target’s x, y, angle Output: Robot angle, speed C:\Core\Massey Papers\159302\Assignments 2008\Assign #2 - 2008\Robot Navigation - v.9.4 - FL-AStar

13. Hybrid Fuzzy A* 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. Genetic Algorithm • Let’s see the demonstration for a GA that maximizes the function n =10 c = 230 -1 = 1,073,741,823

16. Simple GA Example Fitness Function or Objective Function • 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

17. Test Problem Characteristics • With a string length=30, the search space is much larger, and random walk or enumeration should not be so profitable. • There are 230=1.07(1010) 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 1.05 percent of the points have a value greater than 0.9.

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

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

20. Identifying Colour Objects FIRA RoboWorld Congress & CIRAS 2005 with Fuzzy Colour Contrast Fusion

21. Robot Soccer Set-up IIMS Lab 7 Overhead Camera Fluorescent lamps Colour objects www.Fira.net Exploratory environment is indoor – room totally obstructed from sunlight Multiple monochromatic light sources – fluorescent / fluoride lamps Colour Object Recognition (Recognition speed: < 33ms) *

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

23. Colour as the machine sees it Colour constancy is inherent in us humans, but not in cameras. Yellow object turns pale under strong white illumination Color is not captured by the camera as we humans see it. 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 Dark Other Factors: Lens focus Bright Object rotation Dim Quantum electrical effects Shadows Presence of similar colours We need to automatically compensate for the effects of varying illumination intensities in the scene of traversal *

25. Recent Developments Experiments performed at IIMS Lab 7 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

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… Robots at Massey Old system C:\Core\Research\Conferences\ICONIP08