Trial Lecture - NTNU

Autonomous Underwater Robotics:
State of the Art and Future Challenges
PhD Trial Lecture
Arnfinn Aas Eielsen
Department of Engineering Cybernetics, NTNU
November 26, 2012

Outline
Introduction
Goals and Motivations
Groups/Projects With Recent Publications
Modeling
Low-Level Control
Sensor Systems
Simultaneous Localization And Mapping (SLAM)
Other Topics
Conclusions
References

Definitions
Underwater robotics typically refers to Unmanned Underwater Vehicles (UUVs).
At this time, two types of UUVs are in common usage:
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Remotely Operated Vehicles (ROVs)
Autonomous Underwater Vehicles (AUVs)
Remotely Operated Vehicles (ROVs) are tele-operated vehicles, that can be used
to perform inspection and intervention missions under water, by being a mobile
platform for sensors and manipulators.
An ROV is an Underwater Vehicle-Manipulator System (UVMS).
Autonomous Underwater Vehicles (AUVs) are autonomous vehicles, which are
used as mobile sensor platforms that can undertake survey and inspection
missions under water, without human interaction.
Autonomous underwater robotics, is here defined as an autonomous UVMS.
Essentially: combining the capabilities of ROVs and AUVs.

Applications
Dirty – Dangerous – Distant – Dull
Examples of areas:
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Deep water (adverse conditions for communication to surface)
Under ice (inaccessible area for surface vehicles)
Nuclear plant fuel rod storage tanks (hazardous environment)
Examples of tasks:
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Salvage and recovery/retrieval (clearing debris, retrieving objects)
Inspection (fish farming, mine detection, pipeline and hull inspection)
Intervention on subsea installations (valve operations, pipeline repair)
Survey (seafloor mapping, seismology, oceanography)
Observation (aiding undersea rescue, threat detection, marine biology)
Communications (placing acoustic markers, relaying)
Yuh et al. [2011], Sheridan and Verplank [1978]

Examples of Existing AUVs
(a) The Hybrid ROV Nereus (Woods
Hole Oceanographic Institute).
(b) HUGIN (Kongsberg Maritime).
(c) The Bluefin HAUV prototype
(Bluefin Robotics Corporation).
(d) Webb Research Thermal Glider
(Teledyne Webb Research).
(e) RoboLobster (Office of Naval
Research).
(f) Robofish (University of
Washington).
Yuh et al. [2011], Morgansen et al. [2007]

Applications
An ROV performing a valve operation on a subsea structure.
Source: Frank van Mierlo (Public Domain)

Goal
An overall goal for unmanned vehicles is increased autonomy.
Essentially getting rid of the infrastructure needed for tele-operation, expanding
the capabilities of underwater robotics.
Source: Schilling Robotics

Motivations
Use of ROVs is limited due to:
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very high operational costs (approx. €8000 per day)
large crew and specialized mother vessel (dynamic positioning)
can not operate in ice covered areas
operator fatigue (even with auto-heading, auto-depth, etc.)
practical and safety issues (tether management)
time-delay in human-machine interaction (loss of precision)
limited depth (limited capability of tether and pressure hull)
Yuh and West [2001], Antonelli et al. [2008], Antonelli [2006], Yuh et al. [1998], Prats et al. [2011a],
Sheridan and Verplank [1978]

Autonomy – Definitions
Types of autonomy:
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Energy autonomy: reliable power sources and low power consumption
Navigation autonomy: localization without estimate error growth
Decision autonomy: solving tasks and fault detection and tolerance
Levels of automation/autonomy:
HIGH 10 The computer decides everything and acts autonomously,
ignoring the human.
9 Informs the human only if it, the computer, decides to.
8 Informs the human only if asked, or
7 executes automatically, then necessarily informs the human, and
6 allows the human a restricted time to veto before automatic
execution, or
5 executes the suggestion if the human approves, or
4 suggest one alternative.
3 Narrows the selection down to a few, or
2 the computer offers a complete set of decision/action alternatives, or
LOW 1 the computer offers no assistance:
human must take all decision and actions.
Parasuraman et al. [2000], Sheridan and Verplank [1978], Hagen et al. [2009]

Autonomy – Goals
The overall goal for autonomous UVMSs is to be able to issue very high-level
commands, e.g.,
“move from location A to location B”, “inspect the pipeline”, “unplug the
connector”, “retrieve the treasure chest from the sunken pirate ship”
without having to specify all the sub-task needed to solve the mission (like to
ROV operator does now, or the the driver or a car).
Best current examples might be the EUREKA Prometheus Project, DARPA
Grand Challenge, and the Google Driverless Car.
Intermediate goals will be to find ways to assist ROV operators
(semi-autonomy); letting the ROV operators focus on higher level tasks, rather
than controlling everything.
Station keeping/dynamic positioning, automatically solving kinematic redundancy
and trajectory generation problems, compensating for time-delay (augmented
reality, “predictor diplays”), ...
Luettel et al. [2012], Yuh and West [2001], Hagen et al. [2009], Marani et al. [2009], Wang et al. [1995],
Prats et al. [2012a], Candeloro et al. [2012], Sheridan and Verplank [1978]

Research Areas – Autonomous Underwater Robotics
Related to control engineering
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Modeling + robust and adaptive control + singularity free representations
Sensor fusion + disturbance estimation/rejection
Computer vision + simultaneous localization and mapping (SLAM)
Trajectory planning + obstacle avoidance + kinematic redundancy
Mission control systems (high-level control)
Other examples (computer science, electrical, mechanical, chemical, ...):
Sensors, imaging, machine vision, real-time operating systems, dexterous
manipulator systems, efficient thrusters, lightweight structures, high-density
power sources, virtual reality, human machine interfaces, artificial intelligence, ...
Overlapping with research in other fields, especially other mobile robots:
◮ Unmanned Ground Vehicles (UGVs)
◮ BigDog (Boston Dynamics)
◮ Google Driverless Car (Google)
◮ Sojourner (NASA)
◮ Unmanned Aerial Vehicles (UAVs)
◮ Black Hornet (Prox Dynamics)
◮ Predator (General Atomics)

Groups/Projects With Recent Publications
TRIDENT/RAUVI (Reconfigurable Autonomous Underwater Vehicle for
Intervention Missions)
Co-ordinated by University of Jaume I, Castellón de la Plana, Spain
◮ Vehicle: Girona 500
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3 to 8 thrusters + 4 DOF manipulator (ARM5E)
◮ Final sea trials in October 2012
SAUVIM (Semi-Autonomous Underwater Vehicle for Intervention Missions)
University of Hawaii at Manoa, USA
◮ 8 thrusters + 7 DOF manipulator (MARIS 7080)
◮ Sea trials in January 2010
ALIVE (Autonomous Light Intervention VEhicle)
Hitec Framnæs, Cybernetix, Ifremer, Heriot-Watt University, Joint Research
Centre (European Commission)
◮
5 thrusters + 6 DOF manipulator
◮ Sea trials in October 2003
Marani et al. [2009], Prats et al. [2012a], Evans et al. [2003], Prats et al. [2011a]

Movies - ALIVE
The movies demonstrate several features:
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Localization using acoustic signals.
Navigating to the proximity of the target.
Finding the target and approaching.
Visual servo control for station keeping.
Disturbance rejection, compensating for underwater current.
Visual servo control for manipulating/interacting with an ROV panel (valve
operations).
http://www.youtube.com/watch?v=NuVG15Sf9U0

Movies - TRIDENT
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Surveying – simultaneous
localization and mapping (SLAM).
Localization using maps, INS, DVL.
Navigating to the proximity of the
target.
Finding the target and approaching.
Visual servo control for station
keeping.
Visual servo control for interacting
with and retrieving a Black Box
mock-up.
Virtual/augmented reality for
mission control.
Not shown: Synchronous motion with
surface vessel (for SLAM).
http://www.youtube.com/watch?v=jdt_Gy-YjUI
Prats et al. [2011b]

Hardware Architecture – SAUVIM
Kim and Yuh [2004]

Software Architecture – RAUVI
Prats et al. [2012b]

Modeling
Vehicle dynamics:
φ (roll)
υ (surge)
M v ˙ν + C v(ν)ν + D v(ν)ν + g v(RB) I = τ v
[ ]
ν = ν T T
T
1 , ν 2 = [u, v, w, p, q, r]
T
ν = J e(RB) I ˙η ⇒ ˙η = J −1 e (RB)ν
I
[ ]
η = η T T
T
1 , η 2 = [x, y, z, φ, θ, ψ]
T
θ (pitch)
υ (sway)
yb
ψ (yaw)
ω (heave)
zb y
η1
z
x b
x
Vehicle-manipulator dynamics:
M(¯q) ˙ζ+C(¯q, ζ)ζ+D(¯q, ζ)ζ+g(¯q, R I B) = τ
y b
x b
ζ =
[ν T 1 , ν T 2 , ˙¯q T] T
z b
Euler angle representation has
singularities → non-minimal
representations such as quaternions
y
z
x
x 1
z n
y n
x n
Antonelli et al. [2008], Antonelli [2006], Feldman [1979]
y 1
z 1

Modeling – Some Properties
Due to hydrodynamic effects, the mass matrix must include added mass. e.g.:
M =
M
} {{ RB
}
+ M
}{{} A
rigid body added mass
Similarly for the Coriolis and centripetal contribution, e.g.:
C = C RB + C A
The damping matrix D should include hydrodynamic damping effects:
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skin friction
vortex shedding damping
viscous damping (drag and lift)
(surface: potential damping + wave drift damping)
Currents is a major disturbance under water. Assumed to be irrotational and
constant. Included by considering the relative velocity ν r.
[ ]
νc I I T T
= v c1 , 0
T ⇒ νr = ν − RI B νc
I
Gravity and buoyancy forces are added to the vector g(¯q, R I B).
Antonelli et al. [2008], Antonelli [2006]

Modeling – Some Properties
For a fully actuated UVMS:
◮ the inertia matrix is symmetric and positive definite, M = M T > 0
◮ the damping matrix is positive definite, D > 0
◮ the matrix Ṁ − 2C is skew symmetric, Ṁ − 2C = −(Ṁ − 2C)T
But only for certain symmetry and parametrization assumptions!
☺ Good: Linearity in parameters (no current):
Φ(¯q, R I B, ζ, ˙ζ)θ = τ
θ ∈ R n θ
☹ Bad: Model complexity/order:
◮ Single rigid body (e.g. vehicle): n θ,v > 100.
◮ UVMS: n θ = (n + 1)n θ,v .
Antonelli et al. [2008], Antonelli [2006], From et al. [2012]

Modeling – Thrusters
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Thruster dynamics is non-linear (not ideal force generators, i.e. not
instantaneous and linear).
◮ Behaves like a sluggish non-linear filter.
◮ Limited power → force saturation.
◮ Can cause limit-cycling under closed-loop control.
For smaller/lighter vehicles and manipulators, the impact of the
“time-constant” and non-linearity can be a significant.
For UVMSs, the vehicle should be fully actuated, to counteract interaction
forces when using the manipulator.
There should be thruster redundancy for failure tolerance → need methods
for thruster allocation.
Yoerger et al. [1990], Healey et al. [1995], Antonelli et al. [2008], Antonelli [2006]

Modeling – Challenges
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uncertainty
◮ poor knowledge of hydrodynamic effects
◮ difficulty of parameter identification
◮ changes due to payload (mission-to-mission or during mission)
validity of model is limited to low velocities
complexity and/or order of the model
kinematic redundancy
singularity free representations
lack of thruster modeling (poor thruster performance)
Antonelli et al. [2008], Antonelli [2006], Sørensen and Refsnes [2009], Yuh and West [2001]

Low-Level Control
Currently (SAUVIM + TRIDENT/RAUVI) proportional-integral-derivative (PID)
control or feed-forward control is used for vehicle and manipulator control.
PID control can take the form (for the vehicle, using quaternions, but usually
done using Euler angles):
∫ t
τ v = ĝ v(RB)
I +K D ˙˜p + K P ˜p + K I ˜p(t ′ )dt ′
} {{ }
0
optional
[
p = η T 1 Q T] [ ]
T
∈ R 7 η1,d − η 1
, ˜p =
Im( ¯Q d · Q)
The optional term improves transient response of sluggish integral action
(e.g. for yaw motion due to buoyancy and gravitation).
The coupling from the manipulator can be compensated using:
¯τ v = τ v + ˆτ m(R I B, q, ζ, ˙ζ)
The assumption is then that the manipulator has a fixed base, and regular
manipulator control schemes are used (kinematic and dynamic control).
Prats et al. [2012b], Marani et al. [2009], Antonelli [2006], From et al. [2010]

Low-Level Control
Challenges:
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large model uncertainty (time-varying, poor parameter estimation)
◮ disturbances (underwater currents, interaction forces, ...)
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dynamic coupling between the vehicle and the manipulator
To improve performance, several non-linear control schemes have been proposed,
based on, e.g.:
◮ sliding-mode/variable structure
Also:
◮ backstepping
◮ neural-networks
◮ feedback linearization
◮ fuzzy logic
Basis for adaptive control laws and reduced order control laws.
Challenges:
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persistency of excitation
high model complexity and/or order
state estimates (observers)
Antonelli [2006], Sørensen and Refsnes [2009], Yuh and West [2001], Antonelli et al. [2008], Jordan and
Bustamante [2009], Antonelli et al. [2004a]

Sensor Systems
Autonomous operation requires perception of the vehicle’s local environment.
Pigeon: keen eyesight + learning capabilities
(vs. Manual Control to Line of Sight)
Skinner [1960], Wasserman [1995]

Sensors Used for Perception (Mission Specific)
Sensors are used to:
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enable motion control (position measurement/localization)
◮ perform a specific mission (inspection, survey, observation, targeting, ...)
Mission specific sensors (underwater manipulation):
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cameras (two for stereo vision)
sonars (various configurations)
Basis for computer vision, and is used for visual servo control to provide
measurements to solve a task (relative position, shapes, colors, orientation, ...)
Examples of tasks: station keeping, pipeline tracking, recovering objects, valve
operations, clearing debris, welding, ...
Prats et al. [2012b], Antonelli et al. [2008], Marani and Choi [2010], Prats et al. [2011a], Horgan and
Toal [2009], Hutchinson et al. [1996]

Simultaneous Localization And Mapping (SLAM)
Simultaneous Localization And Mapping (SLAM): build a map of an unknown
environment and at the same time use this map to navigate.
A key prerequisite for truly autonomous navigation.
It is used by the high-level control layer (mission control) for
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surveying
finding targets of interest
path planning to target
obstacle avoidance
planning the solution of a task
◮ ...
Extracts features (“landmarks”) from image data and correlates them with
position measurements (sensor fusion).
Luettel et al. [2012], Ferreira et al. [2012], Ribas et al. [2008], Ferreira et al. [2011],
Durrant-Whyte and Bailey [2006]

Simultaneous Localization And Mapping (SLAM)
Prats et al. [2012b]

Sensors Used for Localization and Navigation (Motion Control)
Compass: Measures heading relative to geodetic north (gyro compass) or
magnetic north (magnetic compass).
Inertial Measurement Unit (IMU): Provides measurements of linear acceleration
and angular velocity (using accelerometers, gyroscopes, and magnetometers).
Depth sensor: Pressure measurement that can be translated to a vehicle depth.
Altitude and forward-looking sonar: Length measurements to seafloor and
objects.
Doppler Velocity Log (DVL): Measures vehicle velocity relative to the seafloor
and relative water motion.
Global Navigation Satellite System (GNSS): Used to localize the vehicle on the
surface and to initialize the IMU. (Only works on the surface.)
Acoustic positioning: Measures position relative to transponders.
Long BaseLine (LBL), Short BaseLine (SBL), Ultra Short BaseLine (USBL).
SLAM: Position measurements relative to map features.
Kinsey et al. [2006], Ribas et al. [2008], Corke et al. [2007], Grenon et al. [2001], Hunt et al. [1974]

Sensor Fusion
Truly Autonomous 131
Block diagram of the HUGIN integrated inertial navigation system.
SAS CVL
Reset
Delta
Position
DVL
Velocity
(in B)
Gyros
Accelerometers
IMU
Angular
velocity
Specific
force
INS
Navigation
Equations
Decompose
in L
_
Velocity (in L)
Horizontal
position
Attitude
Depth
Compass
_
_
Pressure
sensor
_
Error state
Kalman
filter
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CVL – Correlation Velocity Log
(velocity)
DVL – Doppler Velocity Log
(velocity)
DGPS – Differential GPS
(position)
USBL – Ultra Short BaseLine
(position)
DGPS +
USBL
GPS
Terrain
Navigation
Range
(+bearing)
Underwater
transponder
positioning
Estimates
(of errors in
navigation
equations and
colored sensor
errors)
diagram Hagen of et the al. HUGIN [2009] integrated inertial navigation system.

Sensors Systems – Challenges
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Low bandwidth of the sensor readings.
Lack of precision (and bandwidth) deteriorates manipulator performance.
Errors and noise in measurements:
◮ IMU and compass bias
◮ acoustic signals affected by water temperature, pressure, and salinity
◮ SLAM, DVL and sonar only work close to seafloor
◮ low visibility and occlusion
Fault tolerance (sensor redundancy).
◮ Non-linear models in sensor fusion (extended/unscented Kalman filter +
particle filter + non-linear observers).
◮
Computational requirements (computer vision, SLAM, and sensor fusion).
SLAM must handle increasingly unstructured environments in situations where
GPS-like solutions (e.g. acoustic transponders) are unavailable or unreliable.
Underwater environment the most challenging for SLAM due to reduced sensorial
possibilities and lack reliable features.

Other Topics
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Fault Detection and Tolerance
◮ Few results due to lack of good models
Guidance and Trajectory Generation
◮ Optimization methods
◮ Waypoints and Line of Sight
Multiple Vehicles and Synchronization
◮ Platooning
◮ Relaying
Communications
◮ Low bandwidth for acoustic modems
◮ Limited range (relaying)
Mission Control
◮ Mission planning and specification
◮ Architectures for autonomous operation
Artificial intelligence
Real-time operating systems
Semi-autonomous operation
◮ Relieving operators (simplifying operations and preventing fatigue)

Conclusions – State of the Art
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ALIVE + SAUVIM + TRIDENT/RAUVI
Experimentally proven autonomous UVMSs
Fairly basic methods:
◮ Control – PID
◮ Sensor fusion – simple models and Kalman filters
◮ Computer vision – well defined geometrical shapes
◮ Autonomy – Petri nets (finite state machines)
◮ Kinematic redundancy – limiting the workspace
◮ ...

Conclusions – Future Challenges
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Mathematical modeling
◮ Singularity free representations
◮ Kinematic redundancy
◮ Thrusters
◮ Complexity
◮ Uncertainty
◮ Correct modeling retaining good model properties
(boundedness and skew-symmetry)
More advanced or different control laws
◮ Adaptive, robust, lower order (e.g. L 1 -adaptive)
Sensor systems (bounded error growth)
Non-linear observers (e.g. particle filters)
Fault detection
Simultaneous localization and mapping (SLAM)
Autonomy (simple task specification and more intelligent behavior)
Yuh and West [2001], Antonelli et al. [2008], Antonelli [2006], Yuh et al. [1998], Prats et al. [2011a]

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