Presentation Outline

• Introduction• Company Profile• Problem Statement• Proposed solution• Cost Analysis• Deliverables• Plan• Conclusion

Company ProfileMembers

• Talha Koc•Murat Ozkan• Ahmet Eris•Halit Ates•Mehmet Alp Ekici

Company Profile

Task Distribution• Programming Talha, Murat• Purchasing Alp, Ahmet• Power analysis&design Halit, Alp, Ahmet• RF analysis&design Talha• Mechanical analysis&design Talha• Control analysis Halit, Murat• Hardware Testing All• R&D, Documentation All

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

Objectives

• Inexpensive and high quality • Optimize cost and time• High accuracy

-Following line-Map extraction

• Low power consumption

Block Diagram of Solution

MAP EXTRACTION

>>LINE FOLLOWER > SENSORS FOR MAPPING> MAPPING ALGORITHM & DISPLAY> DATA TRANSMISSION

PART LIST

• SENSORS– COLOR SENSOR (3)

• MOTORS– STEPPER MOTOR (2)

• WHEELS– WHEEL (2)– CASTER

LINE FOLLOWER

PART LIST

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

MOTOR UNIT

STEPPER MOTORS (2) WHEELS (2)

CASTER

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

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

CASTER

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

MOTION ALGORITHM

GO FORWARD

TURN

TURN LEFT TURN RIGHT

FORWARD+TURN

GO LEFT GO RIGHT

HEAD FORWARD

MAP EXTRACTION

> LINE FOLLOWER

>> SENSORS FOR MAPPING> MAPPING ALGORITHM & DISPLAY> DATA TRANSMISSION

Sensor data

Why optical mouse sensor?

• Resolution is independent of encoder• Not dependent on wheel size• Installation is easy • Gives accurate incremental 2-D displacement

Features of optical mouse sensor

• Optical navigation technology• High reliability• Low cost• High speed motion detector • High resolution

Reading Distance from OMSOptical Mouse resolution-> 1600 counts per inch -> 630 counts per cm

Example: If we read 64 counts in registerthis means that our car has moved 64/630 cm.

0,101cm

Why digital compass?

ADVANTAGES• Easy to implement• Less sensitive to vibrations• High resolution• Low powerDISADVANTAGES• Requires calibration• Affected from magnetic material

Validity of data

MAP EXTRACTION

> LINE FOLLOWER > SENSORS FOR MAPPİNG> > MAPPING ALGORITHM&DISPLAY > DATA TRANSMISSION

Mapping & Display

“Scientist discover the world that exists; engineers create the world that never was.”

(Theodore von Karman )

Block Diagram

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

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)

Mapping Algorithm

• To model robots next position,we need:– Δx and Δy positions– angle α°

• Hardware: OMS-> Δx & Δy V2Xe-> α°

Mapping Algorithm(cont.)

Area Calculation

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),

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

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

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

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

Sample GUI (beta)

MAP EXTRACTION

> LINE FOLLOWER > SENSORS FOR MAPPİNG> MAPPING ALGORITHM&DISPLAY >> DATA TRANSMISSION

RF Block Diagram

Data:• OMS Measurement • Digital Compass Measurement

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

Microcontroller & ATX-34S Connection

ARX-34 & MAX232 Connection

Gantt Chart

Cost AnalysisName Quantity Unit Price (TL)

Stepper Motor 2 20

PIC 1 12

NiMH Battery 4 2

CNY70 3 2

L298 2 2

L297 2 2

Robot Chassis 1 20

Optical Mouse Sensor 1 7

ATX-34 RF Receiver 1 7

ARX-34 RF Transmitter 1 10

Digital Compass 1 75

RS232-Interface 1 15

Other Components 1 30

TOTAL TOTAL 238 YTL

Power Consumption

≈ 4-5 Watt(≈45 Minutes)

Deliverables

• Mobile Robot• User’s Manual• PC Connected Hardware• Warranty Document• Rechargeable Battery Pack

Thanks and Questions

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