Underwater manipulation

Underwater manipulation

Gianluca Antonelli

Universita di Cassino & ISME

[email protected]

http://webuser.unicas.it/lai/robotica

http://www.isme.unige.it

http://www.eng.docente.unicas.it/gianluca antonelli

Gianluca Antonelli BiogradNaMoru, 8 October 2015

mailto:[email protected]

http://webuser.unicas.it/lai/robotica

http://www.isme.unige.it

http://www.eng.docente.unicas.it/gianluca_antonelli

ISME in brief

Italian Joint Research Unit established in 1999

Sites:

AnconaCassinoFirenzeGenovaLeccePisa


ISME in brief

SEA Lab

Joint Italian Navy/ISME located in La Spezia

No need of advance area clearance

Availability of Navy support personnel

Some restrictions (activities/personnel to be listed in advance, no working at nights. . . )


ISME in brief

A selected map of projects logos. . .


Marine Autonomous Robotics for InterventionS

PRIN20102011


Marine Autonomous Robotics for InterventionS

PRIN20102011


Effective Dexterous ROV Operations in Presence of

Communications Latencies

H2020BG2014


Effective Dexterous ROV Operations in Presence of

Communications Latencies

H2020BG2014


Robotic subsea exploration technologies

H2020SC52014

mineral and raw material exploration and recovery (in negotiation)


Outline

Motivation

Inverse Kinematics

A possible kinematic solution: NSB behavioral control

Simulation/experiments


Applications

Where uw manipulation is used/needed:

Oil & gas industry

Renewable energy

Power/communication cables

Fisheries & aquaculture

Archaeology

Security

Natural science/biology

Decommissioning

Diver assistance


State of the art

aged approach

off-shore operator acts on the vehicleoff-shore operator acts on the arm motors (!)voice coordination between the twomanned visual feedback


State of the art

Recent approach

vehicle in automatic station keeping or dockedoff-shore operator with a master/slave architecture


State of the art

Effort for working class vehicles

13 peoples on 24 hours4 to 6 weeksROV crew work 12 hours a day - 7/71 day of operation costs 100÷ 300 ke


Objective

Autonomously (as much ass possible. . . ) achieve complex operations


Space, aerial and underwater vehicle-manipulators

DLRCanadian Space Agency

ALIVE

normal robots but

floating base

kinematic coupling

dynamic coupling

unstructured environment


Floating robots kinematics

Oi

η1

ηee

❅❅❘endeffector velocities

❍❍❍❍❍❍❍❍❍❍❍❍❥Jacobian

system velocitiesηee =

[ηee1

ηee2

]= J(RI

B, q)ζ ζ =

ν1

ν2

q


UVMS dynamics in matrix form

M(q)ζ +C(q, ζ)ζ +D(q, ζ)ζ + g(q,RIB) = τ

formally equal to a groundfixed industrial manipulator 1

however. . .

Model knowledge

Bandwidth of the sensor’s readings

Vehicle hovering control

Dynamic coupling between vehicle and manipulator

External disturbances (current)

Kinematic redundancy of the system

1[Siciliano et al.(2009)Siciliano, Sciavicco, Villani, and Oriolo] [Fossen(2002)][Schjølberg and Fossen(1994)]


Dynamics

Movement of vehicle and manipulator coupled

movement of the vehicle carrying the manipulator

law of conservation of momentum

Need to coordinate

at velocity level ⇒ kinematic control

at torque level ⇒ dynamic control 2

2[McLain et al.(1996b)McLain, Rock, and Lee][McLain et al.(1996a)McLain, Rock, and Lee]


A first solution

Assuming the vehicle in hovering is not the best strategy to e.e. finepositioning3, better to kinematically compensate with the manipulator

3[Hildebrandt et al.(2009)Hildebrandt, Christensen, Kerdels, Albiez, and Kirchner]Gianluca Antonelli BiogradNaMoru, 8 October 2015

Outline

Motivation

Inverse Kinematics




Kinematic control scheme

second. tasks

ηd, qd τ η, q

IKmain task

Control

Output of IK (Inverse Kinematics) is the position/velocity to becontrolled by the actuators (vehicle thrusters and joints’ torques)

Btw, torque level usually not available ⇒ kinematic controller


Kinematic control in pills

✛✚

✘✙

ζ

❘

✛✚

✘✙

σ

Starting from a generic m-dimensional task (e.g., the e.e. position)

σ = f(η, q) ∈ Rm σ = J(η, q)ζ

An inverse mapping is required



✛✚

✘✙

ζ

❘

✛✚

✘✙

σ

✖✕✗✔

■

Starting from a generic m-dimensional task (e.g., the e.e. position)

σ = f(η, q) ∈ Rm σ = J(η, q)ζ

An inverse mapping is required



A robotic system is kinematically redundant when it possesses moredegrees of freedom than those required to execute a given task




Redundancy may be used to add additional tasks

✛✚

✘✙

ζ

❘

✛✚

✘✙

σ

✖✕✗✔

■




Redundancy may be used to add additional tasks

✛✚

✘✙

ζ

❘

✛✚

✘✙

σ

✖✕✗✔

■ σa

✚✙✛✘

σb

✙

✖✕✗✔

✶



Classical example: control e.e. position while reconfiguring thestructure with internal motion

Kuka Iiwa



In the redundant case, the equation

σ = Jζ

is solved by

ζ = JT(JJT

)−1

︸︷︷︸J

†

σ +(I − J †J

)

︸︷︷︸N

ζo

i.e., by a pseudoinverse and an arbitrary vector projected onto thenull-space

need for closedloop also. . .


Handling several tasks

Extended Jacobian4

Add additional (6 + n)−m constraints

h(η, q) = 0 with associated Jh

such that the problem is squared with

[σ

0

]=

[J

Jh

]ζ

4[Chiaverini et al.(2008)Chiaverini, Oriolo, and Walker]Gianluca Antonelli BiogradNaMoru, 8 October 2015


Augmented JacobianAn additional task is given

σh = h(η, q) with associated Jh

such that the problem is squared with

[σ

σh

]=

[J

Jh

]ζ



Task priority redundancy resolution


further projected on the the null space of the higher priority one

ζ = J†σ +[Jh

(I − J †J

)]† (σh − JhJ

†σ)

Also known as the exact solution with close similarities to the

convexoptimizationbased methods



Singularity robust task priority redundancy resolution 5


further projected on the the null space of the higher priority one

ζ = J †σ +(I − J†J

)J†

hσh

5algorithmic singularities here. . . [Chiaverini(1997)]Gianluca Antonelli BiogradNaMoru, 8 October 2015


Behavioral algorithms (behavior=task), bioinspired, artificialpotentials, neuro-fuzzy, cognitive approaches, etc.

btw. . . mood ?


Geometrical meaning of the null-space

σ = Jζ with m = 1 and n = 2 is a line! (left)

Range of the pseudoinverse and the null spaces are orthogonal (right)


Comparison between exact and robust solutions

ζ = J†σ +

[

Jh

(

I − J†J)]† (

σh − JhJ†σ

)

ζ = J†σ +

(

I − J†J)

J†

hσh



ζ = J†σ +

[

Jh

(

I − J†J)]† (

σh − JhJ†σ

)

ζ = J†σ +

(

I − J†J)

J†

hσh



ζ = J†σ +

[

Jh

(

I − J†J)]† (

σh − JhJ†σ

)

ζ = J†σ +

(

I − J†J)

J†

hσh


Some issues

Kinematic singularities

Damped least squareSingular-value-decomposition-based filteringOther kind of filtering

Algorithmic singularities

Two different-priority tasks are achievable alone but not together:

ranks of both J and Jh is full but not of

[J

Jh

](still the

inversion of a singular matrix)

Set-based/inequality control 6

Task transition vs continuity/priority

6[Escande et al.(2013)Escande, Mansard, and Wieber,Simetti et al.(2013)Simetti, Casalino, Torelli, Sperinde, and Turetta,Antonelli et al.(2015)Antonelli, Moe, and Pettersen]


But. . .

What are these tasks we are talking about ?


Tasks to be controlled

Given 6 + n DOFs and m-dimensional tasks: End-effector

position, m = 3

pos./orientation, m = 6

distance from a target, m = 1

alignment with the line of sight, m = 2



Manipulator joint-limits

several approaches proposed, m = 1 to n, e.g.

h(q) =n∑

i=1

1

ci

qi,max − qi,min

(qi,max − qi)(qi − qi,min)



Drag minimization, m = 1 7

h(q) = DT(q, ζ)WD(q, ζ)

within a second order solution

ζ = J †(σ − Jζ

)− k

(I − J†J

)([ ∂h∂η∂h∂q

]+

∂h

∂ζ

)

7[Sarkar and Podder(2001)]Gianluca Antonelli BiogradNaMoru, 8 October 2015


Manipulability/singularity, m = 1

h(q) =∣∣det

(JJT

)∣∣(In 8 priorities dynamically swapped between singularity and e.e.)

joints

inhibited direction

singularitysingularity

setclose to

8[Kim et al.(2002)Kim, Marani, Chung, and Yuh,Casalino and Turetta(2003)] [Chiacchio et al.(1991)Chiacchio, Chiaverini, Sciavicco, and



Restoring moments:

m = 3 keep close gravity-buoyancy of the overall system 9

m = 2 align gravity and buoyancy (SAUVIM is 4 tons) 10

f b

f g

τ 2

9[Han and Chung(2008)]10[Marani et al.(2010)Marani, Choi, and Yuh]



Obstacle avoidance m = 1



Workspace-related variablesVehicle distance from the bottom, m = 1Vehicle distance from the target, m = 1



Sensors configuration variables

Vehicle roll and pitch, m = 2Misalignment between the camera optical axis and the target lineof sight, m = 2



Visual servoing variables

Features in the image plane 11

11[Mebarki et al.(2013)Mebarki, Lippiello, and Siciliano,Mebarki and Lippiello(in press, 2014)]


Outline

Motivation

Inverse Kinematics




Behavioral control in pills

Inspired from animal behavior

sensorsbehavior a

actuators

behavior bactuators

behavior cactuators

How to combine them in one single behavior?


Behavioral control in pills

Inspired from animal behavior

sensorsbehavior a

actuators

behavior bactuators

behavior cactuators

How to combine them in one single behavior?


Competitive behavioral control

Behaviors are in competitions and the higher priority can subsume thelower ones12

sensorsbehavior b

ζ2

behavior a

ζ1

behavior c

ζ3 ζd

12[Brooks(1986)]Gianluca Antonelli BiogradNaMoru, 8 October 2015

Cooperative behavioral control

Behaviors cooperate and the priority is embedded in the gains13

sensorsbehavior b

ζ2 ⊗

α2

behavior a

ζ1

supervisor

⊗

α1

behavior c

ζ3 ⊗

α3

∑ ζd

13[Arkin(1989)]Gianluca Antonelli BiogradNaMoru, 8 October 2015

Competitive-cooperative and tasks conflicting

Cooperative always owns an error, can we inherit the benefit ofGianluca Antonelli BiogradNaMoru, 8 October 2015

NSB

Null Space-based Behavioral control

Each action is decomposed in elementary behaviors/tasks

motion reference command for each task

ζd = J †(σd +Λσ

)σ = σd−σ


NSB: Merging different tasks

NSB inherits the approach of the singularity-robust task priorityinverse kinematics technique

ζd = J †a

(σa,d +Λaσa

)

︸︷︷︸+ J

†b

(σb,d +Λbσb

)

︸︷︷︸primary secondary

Thus, defining:

ζa = J †a

(σa,d +Λaσa

)Na =

(I − J†

aJa

)

ζb = J†b

(σb,d +Λbσb

)




ζd = J †a

(σa,d +Λaσa

)

︸︷︷︸+(I − J†

aJa

)

︸︷︷︸J†b

(σb,d +Λbσb

)

︸︷︷︸primary null space secondary

Thus, defining:

ζa = J †a

(σa,d +Λaσa

)Na =

(I − J†

aJa

)

ζb = J†b

(σb,d +Λbσb

)




ζd = J †a

(σa,d +Λaσa

)

︸︷︷︸+(I − J†

aJa

)

︸︷︷︸J†b

(σb,d +Λbσb

)


Thus, defining:

ζa = J †a

(σa,d +Λaσa

)Na =

(I − J†

aJa

)

ζb = J†b

(σb,d +Λbσb

)




ζd = J †a

(σa,d +Λaσa

)

︸︷︷︸+(I − J†

aJa

)

︸︷︷︸J†b

(σb,d +Λbσb

)


Thus, defining:

ζa = J †a

(σa,d +Λaσa

)Na =

(I − J†

aJa

)

ζb = J†b

(σb,d +Λbσb

)

ζd = ζa +Naζb


NSB: Three-task example

ζa = J †a

(σa,d +Λaσ1

)

ζb = J†b

(σb,d +Λbσ2

)

ζc = J †c

(σc,d +Λcσ3

)

Successive projection approach

Na =(I − J †

aJa

)

N b =(I − J

†bJ b

)

ζd = ζa +Naζb +NaN bζc

Augmented projection approach

Jab =

[Ja

J b

]

Nab =(In − J

†abJab

)

ζd = ζa +Naζb+Nabζc



ζa = J †a

(σa,d +Λaσ1

)

ζb = J†b

(σb,d +Λbσ2

)

ζc = J †c

(σc,d +Λcσ3

)


Na =(I − J †

aJa

)

N b =(I − J

†bJ b

)



Jab =

[Ja

J b

]

Nab =(In − J

†abJab

)




ζa = J †a

(σa,d +Λaσ1

)

ζb = J†b

(σb,d +Λbσ2

)

ζc = J †c

(σc,d +Λcσ3

)


Na =(I − J †

aJa

)

N b =(I − J

†bJ b

)



Jab =

[Ja

J b

]

Nab =(In − J

†abJab

)



From behaviors to actions

sensing/perception

elementary behaviors actions

commands

supervisor


Simple comparison: move to goal with obstacle

avoidance

obstacle avoidance

σ1 = ‖p− po‖ ∈ R

σ1,d = d

J1 = rT ∈ R1×2

r =p− po

‖p− po‖

ζ1 = J†1λ1 (d− ‖p−po‖)

N (J1) = I − J†1J1 = I − rrT

move to goal

σ2 = p ∈ R2

σ2,d = pg

J2 = I ∈ R2×2

ζ2 = Λ2

(pg − p

)


Simple comparison: competitive approach



❆❆❆❆❆❯

only movetogoal



❄

only obstacle avoidance



❄

only movetogoal


Simple comparison: cooperative approach



❆❆❆❆❯

only movetogoal



❇❇❇❇◆

linear combination: higher task is corrupted



❄

only movetogoal


Simple comparison: NSB



❆❆❆❆❯

only movetogoal



❇❇❇◆

nullspaceprojection: higher task is fulfilled



❄

only movetogoal


Gain tuning

Cooperative

task a b c

situation 1 α1,1 α1,2 α1,3

sit. 2 α2,1 α2,2 α2,3

sit. 3 α3,1 α3,2 α3,3

sit. 4 α4,1 α4,2 α4,3

NSB

Each behavior tuned as if it was alone butin each situation the priority needs to be designed


Gain tuning

Cooperative

task a b c d

situation 1 α1,1 α1,2 α1,3 α1,4

sit. 2 α2,1 α2,2 α2,3 α2,4

sit. 3 α3,1 α3,2 α3,3 α3,4

sit. 4 α4,1 α4,2 α4,3 α4,4

NSB



Gain tuning

Cooperative

task a b c

situation 1 α1,1 α1,2 α1,3

sit. 2 α2,1 α2,2 α2,3

sit. 3 α3,1 α3,2 α3,3

sit. 4 α4,1 α4,2 α4,3

NSB



Stability analysis

Lyapunov function14

V (σ) = 1

2σTσ > 0 where σ =

[σT

a σT

b σT

c

]T

V = −σT

Ja

Jb

Jc

v = −σTMσ = −σ

T

Λa Oma,mbOma,mc

JbJ†aΛa JbNaJ

†bΛb JbNJ

†cΛc

JcJ†aΛa JcNaJ

†bΛb JcNJ

†cΛc

σ

V < 0 depending on the mutual relationships among the Jacobians

14[Antonelli(2009)]Gianluca Antonelli BiogradNaMoru, 8 October 2015

Interaction


Outline

Motivation

Inverse Kinematics




Numerical simulation on MARIS model:

underwater 6-DOF vehicle + 7-DOF manipulator

Reach a pre-grasp configuration in terms of end-effector position andorientation

priority-1 task: e.e. configuration (m = 6)

priority-2 task: vehicle roll+pitch (m = 2)

priority-3 task: position of joint 2 (m = 1)

only e.e. ⇒

complete solution ⇒


Numerical simulation on MARIS model:

underwater 6-DOF vehicle + 7-DOF manipulator

Cameraman action: keep the object in the field of view

priority-1 task: field of view (m = 2)

priority-2 task: vehicle roll+pitch (m = 2)

priority-3 task: arm manipulability (m = 1)

priority-4 task: mechanical joint limits (m = 7)

animation ⇒


Simulations and experiments within TRIDENT

[Simetti et al.(2013)Simetti, Casalino, Torelli, Sperinde, and Turetta]


Numerical simulation on MARIS model: interaction

within the task-priority approach

An impedance external loop is designed to push a button

Σ0

ΣI

Σee


Numerical simulation on MARIS model: interaction

within the task-priority approach

An impedance external loop is designed to turn a valve

Σ0

ΣI

Σee

have a look at the experiments made by Pedro Sanz, Pere Ridao

and colleagues within TRIDENT


The presented results are the outcome of the work of several

colleagues from the University of Cassino, the Consortium ISME

and PRISMA, the projects DEXROV and MARIS

Filippo Arrichiello, Elisabetta Cataldi, Stefano Chiaverini, Paolo Di Lillo

ISME PRISMA


Bibliography I

G. Antonelli.

Stability analysis for prioritized closed-loop inverse kinematic algorithms forredundant robotic systems.

IEEE Transactions on Robotics, 25(5):985–994, October 2009.

G. Antonelli.

Underwater robots.

Springer Tracts in Advanced Robotics, Springer-Verlag, Heidelberg, D, 3rdedition, January 2014.

G. Antonelli, S. Moe, and K. Pettersen.

Incorporating set-based control within the singularity-robust multipletask-priority inverse kinematics.

In 23th Mediterranean Conference on Control and Automation, pages1132–1137, Torremolinos, S, June 2015.


Bibliography II

R.C. Arkin.

Motor schema based mobile robot navigation.

The International Journal of Robotics Research, 8(4):92–112, 1989.

R.A. Brooks.

A robust layered control system for a mobile robot.

IEEE Journal of Robotics and Automation, 2(1):14–23, 1986.

G. Casalino and A. Turetta.

Coordination and control of multiarm, nonholonomic mobile manipulators.

In Proceedings IEEE/RSJ International Conference on Intelligent Robots andSystems, pages 2203–2210, Las Vegas, NE, Oct. 2003.

P. Chiacchio, S. Chiaverini, L. Sciavicco, and B. Siciliano.

Closed-loop inverse kinematics schemes for constrained redundant manipulatorswith task space augmentation and task priority strategy.

The International Journal Robotics Research, 10(4):410–425, 1991.


Bibliography III

S. Chiaverini.

Singularity-robust task-priority redundancy resolution for real-time kinematiccontrol of robot manipulators.

IEEE Transactions on Robotics and Automation, 13(3):398–410, 1997.

S. Chiaverini, G. Oriolo, and I. D. Walker.

Springer Handbook of Robotics, chapter Kinematically RedundantManipulators, pages 245–268.

B. Siciliano, O. Khatib, (Eds.), Springer-Verlag, Heidelberg, D, 2008.

A. Escande, N. Mansard, and P.-B. Wieber.

Hierarchical quadratic programming: Fast online humanoid-robot motiongeneration.

International Journal of Robotics Research, 2013.


Bibliography IV

T.I. Fossen.

Marine Control Systems: Guidance, Navigation and Control of Ships, Rigs andUnderwater Vehicles.

Marine Cybernetics, Trondheim, Norway, 2002.

J. Han and W.K. Chung.

Coordinated motion control of underwater vehicle-manipulator system withminimizing restoring moments.

In Intelligent Robots and Systems, 2008. IROS 2008. IEEE/RSJ InternationalConference on, pages 3158–3163. IEEE, 2008.

M. Hildebrandt, L. Christensen, J. Kerdels, J. Albiez, and F. Kirchner.

Realtime motion compensation for ROV-based tele-operated underwatermanipulators.

In IEEE OCEANS 2009-Europe, pages 1–6, 2009.


Bibliography V

J. Kim, G. Marani, WK Chung, and J. Yuh.

Kinematic singularity avoidance for autonomous manipulation in underwater.

Proceedings of PACOMS, 2002.

G. Marani, S.K. Choi, and J. Yuh.

Real-time center of buoyancy identification for optimal hovering in autonomousunderwater intervention.

Intelligent Service Robotics, 3(3):175–182, 2010.

T.W. McLain, S.M. Rock, and M.J. Lee.

Coordinated control of an underwater robotic system.

In Video Proceedings of the 1996 IEEE International Conference on Roboticsand Automation, pages 4606–4613, 1996a.

T.W. McLain, S.M. Rock, and M.J. Lee.

Experiments in the coordinated control of an underwater arm/vehicle system.

Autonomous robots, 3(2):213–232, 1996b.


Bibliography VI

R. Mebarki and V. Lippiello.

Image-based control for aerial manipulation.

Asian Journal of Control, in press, 2014.

R. Mebarki, V. Lippiello, and B. Siciliano.

Exploiting image moments for aerial manipulation control.

In ASME Dynamic Systems and Control Conference, Palo Alto, CA, USA,2013.

N. Sarkar and T.K. Podder.

Coordinated motion planning and control of autonomous underwatervehicle-manipulator systems subject to drag optimization.

Oceanic Engineering, IEEE Journal of, 26(2):228–239, 2001.

I. Schjølberg and T. Fossen.

Modelling and control of underwater vehicle-manipulator systems.

In in Proc. 3rd Conf. on Marine Craft maneuvering and control, pages 45–57,Southampton, UK, 1994.


Bibliography VII

B. Siciliano, L. Sciavicco, L. Villani, and G. Oriolo.

Robotics: modelling, planning and control.

Springer Verlag, 2009.

E. Simetti, G. Casalino, S. Torelli, A. Sperinde, and A. Turetta.

Floating underwater manipulation: Developed control methodology andexperimental validation within the TRIDENT project.

Journal of Field Robotics, 31(3):364–385, 2013.


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