Supervisor: Prof. Zexiang Li (ECE)

Student: Mingyu Wang (ECE)

Shucheng Zhu (CPEG)

Outline Inspiration

Workflow

Hardware

Mathematic Model

Tests and Simulations

Chemical Sensor Selection

Conclusion

Inspiration

City A

monitoring points

City A

moving path

InspirationMovable Water Monitoring point

GPS Robotic Water Analyzer

Robotic Boat Platform PC Base Station

Workflow

Hardware Assembling & Programming

Arm9 & Base Station UI

Programming

Mathematic Model

Testing & Simulation

Sensor Installing

Hardware

RS232: speed & heading commands

GPS Sensor

Digital Compass

Rudder

Motor

Arm9

Atmega128User control & Reporting (RF)

RS232

RS232

RS232

ADC

PWM

Hardware Arm9 S3C2440 processor

Operating system: WinCE 5.0

Program language: C#

Input: GPS Sensor (position), Digital Compass (heading),

Distance Sensor & water probes

Output: SendingCommand to Atmega128 control board

Data Saving to SD card

Hardware Atmega128

processor

Program language: C

Hardware Navigation Components

Digital Compass

GPS

Hardware Motor System

Servo Motor: Rudder

DC Motor: Propeller

Motor Driver, Water Cooling System

PC Base Station

420D\r85B\r

22.3415667N\r114.233124E\r

29H

RF_Interface

Waypoint Manager

Data Display

Speed & Rudder Angle

Excel Writer

RS232

RF Module

PowerAntenna

Mathematic Model

SurgeRoll

HeaveSway

YawPitch

3 degree of Freedom

111111)longi(longiX origincurrentcurrent

)cos(longi111111)lati(latiY currentorigincurrentcurrent

2BA

2BABA )Y(Y)X(Xdis

Earth fixed coordinate system

Motion Estimation

d

k

k1k

1kkk1k

1kkk1k

kk

k

1k

1k

1k

k

)ω(θh

)h)/2cos(ωos)d(θX

)h)/2sin(ωin)d(θX

0

θ

v

,

h

Y

X

f wwx

Motion Estimation

y = 0.027xR² = 0.999

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

-30 -20 -10 0 10 20 30

1.1

1.2

1.3

1.4

1.5

1.6

1.7

1.8

1.9

2

-30 -20 -10 0 10 20 30

113.9736

113.9736

113.9736

113.9736

113.9736

113.9736

113.9736

113.9736

113.9736

113.9737

113.9737

22.393 22.39302 22.39304 22.39306 22.39308 22.3931 22.39312

Kalman Filter

k1k1k1k|1-kk1k|k wUBxFx

1kk1k|1-kk1k|k QFPFPT

1k

T

k1k|kk

T

k1k|kk )(

RHPHHPK

)( 1k|kkkk1k|kk|k xHzKxx

1-k|kkkk|k )PHK(IP

Estimation Phase:

Update Phase:

Kalman Filter

Kalman Filter Pseudo-codeSet R; //measurement noise Set Q; //process noise, tuned valuex(1,1)=z(1);p(1,1)=R;for k=2:n;

x(k,k-1)=x(k-1,k-1);p(k,k-1)=p(k-1,k-1)+Q;kg(k)=p(k,k-1)/(p(k,k-1)+R);x(k,k)=x(k,k-1)+kg(k)*(z(k)-x(k,k-1));p(k,k)=(1-kg(k))*p(k,k-1);

end;

Static GPS Test

0

2

4

6

8

10

12

14

0 0.5 1 1.5 2

0

2

4

6

8

10

12

14

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8

0.4

0.5

0.6

0.7

0.8

0.9

1

1.1

0 5 10 15 20

0.3

0.35

0.4

0.45

0.5

0.55

0 5 10 15 20

Static test before Kalman Filter After Kalman Filter

latitude (0~12.3m) longitude (0~1.7m)

Error: latitude < 0.5m, longitude < 0.5m

Constant speed motion test

R² = 0.9952

-20

-10

0

10

20

30

40

50

60

70

80

0 20 40 60 80 100 120

R² = 0.9982

-20

-10

0

10

20

30

40

50

60

70

80

0 20 40 60 80 100 120

0.3

0.4

0.5

0.6

0.7

0.8

0.9

0 5 10 15 20 25

0.4

0.42

0.44

0.46

0.48

0.5

0.52

0.54

0.56

0.58

0.6

0 5 10 15 20 25

Constant speed test before KF After KF

Uniform circular motion test

-4

-3

-2

-1

0

1

2

3

4

5

-2 -1 0 1 2 3 4 5 6 7

many points overlap each other

-3

-2

-1

0

1

2

3

4

5

6

-1 0 1 2 3 4 5 6 7 8

-4

-3

-2

-1

0

1

2

3

4

5

-1 0 1 2 3 4 5 6 7

1.5

1.6

1.7

1.8

1.9

2

2.1

0 5 10 15 20

0.61

0.62

0.63

0.64

0.65

0.66

0.67

0 5 10 15 20

Circular motion test raw data Pure estimation After KF

Simulation

0

50

100

150

200

250

300

350

0 50 100 150 200 250 300

A simulating water flow (2 m/s, to the south) is added.

A simulating water flow (0.5 m/s, to the south) is added.

0

50

100

150

200

250

300

0 20 40 60 80 100 120 140 160 180

*Destination: (170, 290)

Kalman filter adjusted by water current

Modified Update Phase Equation:

0

50

100

150

200

250

300

350

0 50 100 150 200

Final SimulationA simulating water flow (2 m/s, to the south) is added.

*Destination: (170, 290)

Control Command

θω1θωΔh

need-1

be-to-need hωθ

,tan2

,tan2

,0

1

1

ndestinatiocurrent

ndestinatiocurrent

ndestinatiocurrent

ndestinatiocurrent

desired

XX

YY

XX

YY

ndestinatiocurrent XX

ndestinatiocurrent XX

ndestinatiocurrent XX

Resent Real Navigation Test

0

50

100

150

200

0 5 10

*Recorded from HKUST Sea Front

Defects of the Current Control Model

0

50

100

150

200

250

300

350

0 50 100 150 200

Follow-the-carrot Control Model- The path is curved by water flow

- It is efficient in static water but not in dynamic water

Improvementdestination destination

Improvementdestination

1Water Speed

Add Chemical Sensors

Cathode – positive pole reaction:

Anode – negative pole reaction:

Total reaction:

*KDS-25 dissolved oxygen & temperature sensor

1

6

11

16

21

26

0 5 10 15 20

Future Work Larger boat platform

Carry more chemical sensors

More batteries to longer the working time

Stronger antenna to communicate longer

Even can go underwater

Acknowledgement Wholehearted gratefulness to Prof. Zexiang Li

My partner: Mingyu Wang

Special thanks to Prof. Xiaoyuan Li

Thanks for your time

Some Achievements Won 2nd Place Hang Seng Green Challenge

Competition

Finalist of 2009 President’s Cup

Q & A