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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
?