2. Presentation Outline Introduction Company Profile Problem Statement Proposed solution Cost Analysis Deliverables Plan Conclusion

3. Company Profile Members Talha Koc Murat Ozkan Ahmet Eris Halit Ates Mehmet Alp Ekici

4. Company Profile Task Distribution ProgrammingTalha, Murat PurchasingAlp, Ahmet Power analysis&designHalit, Alp, Ahmet RF analysis&designTalha Mechanical analysis&designTalha Control analysisHalit, Murat Hardware TestingAll R&D, Documentation All

5. Problem Statement A vehicle that extracts the map of a closed path Fits inside a 1m by 1m square 1 cm accuracy No hard wiring The vehicle will not start its operation on the path No overhead camera Area of map

6. Objectives Inexpensive and high quality Optimize cost and time High accuracy - Following line -Map extraction Low power consumption

7. Block Diagram of Solution

8. MAP EXTRACTION >>LINE FOLLOWER > SENSORS FOR MAPPING > MAPPING ALGORITHM & DISPLAY > DATA TRANSMISSION

9. PART LIST SENSORS – COLOR SENSOR (3) MOTORS – STEPPER MOTOR (2) WHEELS – WHEEL (2) – CASTER

10. LINE FOLLOWER

11. PART LIST

12. COLOR SENSORS Detection of line Will be 3 - 5 mm above ground Placed in a row; 2 cm front of centre line Separated by 1 cm; left to right

13. MOTOR UNIT STEPPER MOTORS (2) WHEELS (2) CASTER

14. MOTORS Stepper Motors – Controlled by digital input – Can be driven slow – Can be used without gearbox – Low error fraction – Having no contact brushes increases life-time Will be placed 2 cm behind centre line

15. WHEELS Rubber wheel for high friction Small size (r=1cm) for good resolution Will be connected to motors separately Like motors; placed 2 cm behind centre line Will keep chassis 3-5 mm above ground

16. CASTER To support robot Easily moveable To keep robot balanced Placed on the middle, 2 cm away from front

17. MOTION ALGORITHM

18. GO FORWARD

19. TURN TURN LEFTTURN RIGHT

20. FORWARD+TURN GO LEFTGO RIGHT

21. HEAD FORWARD

22. MAP EXTRACTION > LINE FOLLOWER >> SENSORS FOR MAPPING > MAPPING ALGORITHM & DISPLAY > > DATA TRANSMISSION

23. Sensor data

24. Why optical mouse sensor? Resolution is independent of encoder Not dependent on wheel size Installation is easy Gives accurate incremental 2-D displacement

25. Features of optical mouse sensor Optical navigation technology High reliability Low cost High speed motion detector High resolution

26. Reading Distance from OMS Optical Mouse resolution-> 1600 counts per inch -> 630 counts per cm Example: If we read 64 counts in register this means that our car has moved 64/630 cm. 0,101cm

27. Why digital compass? ADVANTAGES Easy to implement Less sensitive to vibrations High resolution Low power DISADVANTAGES Requires calibration Affected from magnetic material

28. Validity of data

29. MAP EXTRACTION > LINE FOLLOWER > SENSORS FOR MAPPİNG > > MAPPING ALGORITHM&DISPLAY > DATA TRANSMISSION

30. Mapping & Display “Scientist discover the world that exists; engineers create the world that never was.” (Theodore von Karman )

31. Block Diagram

32. Localization – Position Estimation Q: How to estimate robot’s pose with respect to a global frame? 1.Absolute Pose Estimation (GPS,Landmarks,Beacons) 2.Relative Pose Estimation (Dead Reckoning) 3.Appropriate Combination of 1 & 2

33. Dead Reckoning Used extensively in robotic applications – Classical Use: Wheel Encoders – Advantages: Simple,cheap,easy – Drawback: Accumulation of errors Solution: – High presicion optical mouse sensors (ADNS3080) – No kinematic errors as in wheel encoders – Post filtering ( Kalman/Markov Filters)

34. Mapping Algorithm To model robots next position,we need: – Δx and Δy positions – angle α° Hardware: OMS-> Δx & Δy V2Xe-> α°

35. Mapping Algorithm(cont.)

37. Area Calculation

38. Error Considerations Is Optical Mouse Sensor good enough to satisfy +-1cm accuracy? F. A. Kanburoglu, E. Kilic, M. Dolen, M., A. B. Koku, A Test Setup for Evaluating Long-term Measurement Characteristics of Optical Mouse Sensors. "Journal of Automation, Mobile Robotics, and Intelligent Systems", 1, (2007),

39. Error Considerations (cont.) Pose = Distance + Angle measurements These measurments have ERRORS or NOISE included. What to do? Kalman Filter -> Smart Way of processing data Makes distinction between reliable data & unreliable data Smooths out the effect of noise

40. Kalman Filter Simulation for V2Xe Assumption of noisy data with %2 error Tested for hypothetical values in MATLAB First Order Kalman Filter,R=100First Order Kalman Filter,R=2

41. Display Software The software on PC side: – Processing of the raw measurement data – Calculation of the next position according to the state equations – Apply filtering, if necessary – Display the new position on screen in simultaneously

42. Display Software Testing: MATLAB is used for map building,filtering MATLAB Serial Port I/O Interface The CAS Robot Navigation Toolbox (GPL) Final Software: Written in C++ by Wh.Electronics With a GUI showing map building process

43. Sample GUI (beta)

44. MAP EXTRACTION > LINE FOLLOWER > SENSORS FOR MAPPİNG > MAPPING ALGORITHM&DISPLAY >> DATA TRANSMISSION

45. RF Block Diagram Data: OMS Measurement Digital Compass Measurement

46. Why ATX-34S & ARX-34 ? High Frequency Stability Low Cost (ATX->7TL, ARX->10TL) Low Battery Consumption(max 10mA) Easy Integration with PIC Good Documentation

47. Microcontroller & ATX-34S Connection

48. ARX-34 & MAX232 Connection

49. Gantt Chart

50. Cost Analysis NameQuantityUnit Price (TL) Stepper Motor220 PIC112 NiMH Battery42 CNY7032 L29822 L29722 Robot Chassis120 Optical Mouse Sensor17 ATX-34 RF Receiver17 ARX-34 RF Transmitter110 Digital Compass175 RS232-Interface115 Other Components130 TOTAL 238 YTL

51. Power Consumption ≈ 4-5 Watt (≈45 Minutes)

52. Deliverables Mobile Robot User’s Manual PC Connected Hardware Warranty Document Rechargeable Battery Pack

53. Thanks and Questions ?