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ROV Control Systems

CeSOS Highlights and AMOS Visions

Trondheim, 27-29th May 2013

Fredrik Dukan

Centre for Autonomous Marine Operations (AMOS), Department of Marine Technology, NTNU

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Fredrik Dukan

M.Sc. Marine Cybernetics, NTNU (2008)

Aker Marine Contractors (2008-10)

PhD Candidate, NTNU, (2010-14)

• Terrain following • Navigation • Implementation

ROV Control Systems

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Outline

Motivation & Challenges

• Actuators & Sensors

ROV System

• Architechture • Navigation, Guidance & Control • Implementation

Control System

• ROV Minerva & 30k

Full Scale Tests

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Motivation, ROV Control System:

• Safety • Performance & Consistency • Time & Cost

Automate underwater operations

• Biology • Geology • Archeology • Oil & gas

Add value to other fields

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Challenges, ROV Control System:

• No GPS • Size and cost limitations

Underwater navigation

• Unknown & complex • Deep water

Environment

• Combined vehicle and manipulator control • Manipulator forces

Manipulation work

• Drag • Spring

Umbilical

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Easy, ROV Control System:

• “Unlimited” power supply • High bandwidth communication

Umbilical

• Lots of computational power • Fall back to remote operation

Top side computers

• Small & slow • No waves • Low risk

Dynamics and environment

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ROV Actuators Top view Side view

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ROV sensors

Inertial measurement unit (IMU)

Doppler velocity log (DVL)

Pressure gauge

Transponder

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Control System Architecture

Signal Processing

Observer

Controller

Guidance System

Thrust Allocation

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Navigation

• Position • Velocities

Vehicle States

• Sensors • Dynamical models

Observer

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Guidance

• Reference models

AtB

• WP management • LOS

WP tracking

• Altitude control • Obstacle avoidance

Terrain following

• Closed-loop

Joystick

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Control

• PID

Controller

• Fixed thrusters • Thruster model • Saturation

Thruster allocation

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Control System Key Topics

• Extended Kalman filter

• Non-linear passive observer

• Particle filter • Integration filters

Observers

• Joystick • Terrain following • Altitude control • Tracking • Path following • Reference models

Guidance

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Level of Human Interaction/Autonomy

• Direct thruster control or TA • Pilot compensates for

• Dynamics • Env. forces

Remotely Controlled (cont.)

• Control system compensates for • Dynamics • Env. forces Operator Guided (cont.)

• DP • AtB • WP tracking Supervised (min-hrs)

• Terrain following • Collision/obstacle avoidance Mission Control

(hrs-days)

• Mission objective • Artificial Intelligence Autonomous

(hrs-yrs)

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ExampleTopic: Altitude Control

• DVL – 4 beams – Sea floor gradient

• Altitude estimation • Guidance law for

desired depth • Depth Control

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ROV Motions & Sea Floor Geometry:

Altitude rate of change:

Altitude:

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Guidance Law for Altitude Control

Desired altitude rate:

Feedforward term for zero roll and pitch :

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Guidance Law for Altitude Control

Depth reference for altitude control (zero roll & pitch):

• Continuous desired depth • Smooth for DVL approximated altitude

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Guidance Law for Altitude Control

Speed reduction

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Altitude Estimation and Control Lawnmower pattern with snapshots of ROV outline:

-30 -25 -20 -15 -10 -5 0 5

0

5

10

15

20

25

East Position [m]

ROV Trace

Nor

th P

ositi

on [m

]

Estimated altitude and depth reference:

0 500 1000 15004.5

5

5.5

6ROV Altitude

t [s]

a [m

]

aCOd

am aCOavg

0 500 1000 150078

80

82

84

86

ROV Depth

t [s]

z [m

]

zd z

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Implementation

• Graphical programming language

• Rapid prototyping

Labview

• Real time controller • FPGA

Compact RIO

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LabView Object-Oriented Programming

• Libraries and classes for controllers, observer, etc.

Modular Design

• ROV specific data • System properties (mass, damping, thruster coeffs., etc.) • Controller/observer gains, etc. • Sensor positions, configurations, communication, etc.

• Easy commisioning of control system on other ROVs

Configuration File

• Communication via TCP/IP • Enables operation from remote location

Separate GUI & Control system

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Frigg GUI

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Experiences from the Field

• Matlab • Tested on Minerva • Early test stage

First DP in 2010

• Labview • ROV as test bed/instrument platform • Tested on 30k • Operational

New Control System in 2012

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ROV Minerva

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ROV 30 k

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Trondheim Harbour Wreck Site

View of sea floor ROV trace and outline

5.7107 5.7108 5.7108 5.7108 5.7109 5.711

x 105

7.0371

7.0371

7.0371

7.0371

7.0371

7.0371

7.0371

7.0371

7.0371

7.0371

7.0371x 10

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East Position [m]

ROV Trace

Nor

th P

ositi

on [m

]

x; y; Axd; yd

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Altitude control/terrain following

ROV trace and outline Sea floor and ROV trace

0 50 100 150 200 250 300 350 400 450 500

50

55

60

65

70

75

ROV Terrain Following Profile

distance [m]

z [m

]

zter rain

zROV

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Photomosaic Example (Tautra, Trondheim Fjord): 10 transects, 1500 pictures, seabed area of 200 square meters (10m x 20m). Applications: - Automatic identification of biological species, underwater flora and fauna; - Relative displacement of pictures can be feed back to the control system to improve position estimation. - Automatic monitoring and surveillance of underwater structures, such as pipelines; - Mapping and overview of underwater wrecks.

Corals (Lopheila Pertusa)

Fish (Sebastes Viviparus)

Sponges (Mycale Lingua)

Sea Urchin

Star Fish: Henricia

Echinus esculentus

Crinoidea

10cm

ROV Filtered Path

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Conclusions

• Master and PhD students • Labview • Full scale testing

Development

• Operational • Multiple ROVs • Value for others

DP System

• Underwater navigation • Combined manipulator & vehicle control • Autonomy

Further work