integrated systems formarineenviroment ancona-cassino...

Patrolling strategies for multiple autonomous vehicles

Giuseppe Casalino, Gianluca Antonelli, Alessandro Marino

Integrated Systems for Marine Enviroment

Ancona - Cassino - Genova - Lecce - Pisa - Trieste - Verona

http://www.isme.unige.it

ISME Milano, 28 august 2011

ISME in brief

Italian Interuniversity research center established in 1999

Sites:

AnconaCassinoGenovaLeccePisaTriesteVerona

Infrastructures from all departements

> 30 researchers (not full time)


ISME main research areas

Underwater robotics

ROVAUVUW manipulationguidance navigation & control

Underwater acoustics

geoacousticsacoustics tomographyimagingsonar

Signal & data processing

geographical informationsystemsdecision support systemsclassification & data fusion


ISME fundings

Within FP5-FP6-FP7: 6 European projects

Several regional and national projects

Technology transfer

Formal agreements with

NATO Undersea Research CenterItalian NavyNational University of SingaporePrivate companies


UAN

fundings : FP7 - Cooperation - ICT/Securitykind : Collaborative Project (STREP)duration : 3 yearsstart : Oct. 2008budget : ≈ 3Me

http://www.ua-net.eu/


TRIDENT

fundings : FP7 - Cooperation - ICT - Challenge 2Cognitive Systems, Interaction, Robotics

kind : Collaborative Project (STREP)duration : 3 yearsstart : March 2010budget : ≈ 3.2Me

http://www.irs.uji.es/trident/


CO3AUVs

fundings : FP7 - Cooperation - ICT - Challenge 2Cognitive Systems, Interaction, Robotics

kind : Collaborative Project (STREP)acronym : CO3AUVsduration : 3 yearsstart : Feb 2009budget : ≈ 2.5Me

http://robotics.jacobs-university.de/projects/Co3-AUVs/index.htm


CO3AUVs - Abstract

The aim of the CO3AUVs project is to provide a significantimprovement on the cooperation control for multiple AUVs. Severalaspects will be investigated, the situation awareness, the deliberationand navigation, the behavioral control, strictly linked with thecommunications issues. As a result, the team of AUVs will cooperate,communicate, it will be robust with respect to failures andenvironmental changes; the key challenge of the project will be anharbor scenario where additional difficulties with respect to open seaapplications arise.


Patrolling

Merriam-Webster

The action of traversing a district or beat or of going the rounds alonga chain of guards for observation or the maintenance of security

Two problems afforded with different approaches

BorderArea


Patrolling

Constraints

Totally decentralizedRobust to wide range of failures

communicationsvehicle lossvehicle still

Flexible/scalable to the number of vehicles add vehicles anytimePossibility to tailor wrt communication capabilitiesNot optimal but benchmarking requiredTo be implemented on a real set-up obstacles. . .


Patrolling

Mathematically strong overlap with (dynamic)

coveragedeploymentresource allocationsamplingexplorationmonitoring

slight differences depending on assumptions and objective

functions


One page on NSB

Null-Space-based Behavioral control is yet another behavioral control

We like it because:

Based on a popular task priority algorithm for industrial robots

Exact decomposition of the task errors no corruption

Rigorous stability analysis

Easy parameter tuning basically decoupled, embed in priority

We developed it. . .

Best performance with large DOFs (not really with planar vehiclesthus. . . )


Second of the one page NSB

A simple experimental outcome:

competitive method

0 10 20 30 40

−15

−10

−5

0

5

10

obstacle

start

goal




cooperative method

0 10 20 30 40−20

−15

−10

−5

0

5

10

obstacle

start

goal




NSB

0 10 20 30 40−20

−15

−10

−5

0

5

10

obstacle

start

goal


Border patrolling

Solution based on a kind of behavioral control

Patrolling embed in the tasks definition and coordination

No communication allowed by assumption

Experimental results with ground robots

Behaviors

Supervisor

Actions NSB

Actuators Sensors

Real Robot Simulator

Supervisor Level

Action Level

Robot Level


Border patrolling

Definition of several elementary behaviors

Reach frontier

Patrol (counter)clockwise

Teammate avoidance

Friend avoidance

Basically move-to-goal tasks and obstacle-avoidance


Border patrolling

Actions, a frozen priorization of elementary behaviors

sensing/perception

elementary behaviors actions

commands

supervisor


Border patrolling

Different techniques for the supervisor, in charge of switching amongactions

Finite State Automata

C2 !C2

MS2: border not in the visibility area

MS4: robot patrolling

Action Reach

FrontietAction Teammate

Avoidance

!c1 c1

Action Keep

Going

Action Patrol

CW

Action Patrol

CCW

c3

!c3!c2

c2

-!=not operator &=and operatorC1=dist. from friend < saft. areaC2=dist. from border < Visibility Range C3=dist. from border <= Thresholdc1=team. in saft. area c2=team. on left side c3=team. on right side c4=!c2 & !c3

c2 c3

c4

MS3: border in the visibility area

Action Keep

Going

!c1 c1

C3 !C3

!c1

!c1

c1

c1

.

Action Friend

Avoidance

MS1: friend in visibility area

!C1 C1

Action Teammate

Avoidance

Fuzzy logic

0 2 4 6 8 10

0

0.2

0.4

0.6

0.8

1

[m]

Deg

ree

of m

embe

rshi

p

low highmedium

DistancefromBorder

0 2 4 6 8 10

0

0.2

0.4

0.6

0.8

1

[m]

Deg

ree

of m

embe

rshi

p

highlow medium

DistanceFromNeighbor

−1 −0.5 0 0.5 1

0

0.2

0.4

0.6

0.8

1

[]

Deg

ree

of m

embe

rshi

p

onMyLeft onMyRightnotPatrolling

NeighborPosition

0 0.2 0.4 0.6 0.8 1

0

0.2

0.4

0.6

0.8

1

[]

Deg

ree

of m

embe

rshi

p

low highmedium

ReachBorderLevel, KeepGoing, PatrolCW, PatrolCCW

proofs of consistency and completeness


Border patrolling - numerical validation

Dozens of numerical simulations by changing the key parameters:

crowded/solitary patrolling

friends

faults

simultaneous events

sensor noise

border shape

−200 −100 0 100 200

−200

−150

−100

−50

0

50

100

150

200 time = 0.4s

x [m]

y [m

]

−200 −100 0 100 200

−200

−150

−100

−50

0

50

100

150

200 time = 4s

x [m]

y [m

]

−200 −100 0 100 200

−200

−150

−100

−50

0

50

100

150

200 time = 7s

x [m]

y [m

]

−200 −100 0 100 200

−200

−150

−100

−50

0

50

100

150

200 time = 25s

x [m]

y [m

]ISME Milano, 28 august 2011

Border patrolling - experimental validation

Distributed Intelligence LaboratoryUniversity of Tennessee


Border patrolling - benchmarking

Instantaneous Segment Idleness (ISI): time elapsed while thesingle segment is not visited;

Instantaneous Border Idleness (IBI): average of the ISI for eachsegment;

Border Idleness (BI): average of the IBI

Border Idleness

[s]

Number of Robots6 9 12 18 21 24 27 30 33

5

10

15

15

NSBOptimum


Border patrolling - benchmarking

Coverage index (ratio between the patrolled and the empty border) incase of friends and faults

Coverage Index

[s]5 10 15 20 25 30

0.2

0.4

0.6

0.8

1

Coverage Index

[s]5 10 15 20 25 30

0.2

0.4

0.6

0.8

1


Area patrolling - introduction

Probabilistic approach

Voronoi-based

Map-based

Communication required only to exchange key data of the maps

Motion computed to increase information

Framework to handle

Spatial variability patrol more often gatesTime-dependency come back to visited placesAsynchronous spot visiting demand

Originally developed as adaptive sampling strategy


Voronoi partitions I

The Voronoi partitions (tessellations/diagrams) are a subdivisions of aset S characterized by a metric with respect to a finite number ofpoints belonging to the set

the cells union gives back the set

the cells intersections is null

computation of the cells is adecentralized algorithm withoutcommunication needed


Voronoi partitions II

Spontaneous distribution of restaurants


Voronoi partitions III

Voronoi in nature


Voronoi partitions IV

Voronoi in art: Escher


Area patrolling - background I

Variable of interest is a Gaussian process

Given the points of measurements done. . .

Sa ={(xa1 , t

a1 ), (x

a2 , t

a2 ), . . . , (x

ana

, tana

)}

and one to do. . .

Sp = (xp, t)

Synthetic Gaussian representation of the condition distribution

{

µ̂ = µ(xp, t) + c(xp, t)TΣ−1

Sa(ya − µa)

σ̂ = K(f(xp, t), f(xp, t))− c(xp, t)TΣ−1

Sac(xp, t)

c represents the covariances of the acquired points vis new one


Area patrolling - description I

The variable to be sampled is a confidence map

Reducing the uncertainty means increasing the highlighted term

µ̂ = µ(xp, t) + c(xp, t)TΣ−1

Sa(ya − µa)

σ̂ = K(f(xp, t), f(xp, t)) − c(xp, t)TΣ−1

Sac(xp, t)︸︷︷︸

ξ

− > ξ example


Area patrolling - description II

Distribute the computation among the vehicleseach vehicle in its own Voronoi cell

Compute the optimal motion to reduce uncertaintyseveral choices possible


Area patrolling - accuracy

Based on:

communication bit-rate

computational capability

area dimension


Area patrolling - numerical validation

Dozens of numerical simulations by changing the key parameters:

crowded/solitary patrolling

faults

obstacles

sensor noise

area shape/dimension

communication bit-rate


Area patrolling - experimental validation

Laboratory of Robotics and Systems in Engineering and ScienceIST, Technical University of Lisbon


Area patrolling - experimental validation

3 Medusas

switched off only forlow battery

virtual obstacle

Laboratory of Robotics and Systems in Engineering and ScienceIST, Technical University of Lisbon


Area patrolling - some benchmarking

With a static field the coverage index always tends to one

0 200 400 600 800 1000

0.2

0.4

0.6

0.8

1

step

[]

Coverage Index


Area patrolling - some benchmarking

Comparison between different approaches

00

LawnmowerProposedRandomDeployment0.5

1.5

2

200 400 600 800 1000 1200

1

[]

step

time varying case

same parameters

lawnmower rigidwrt vehicle loss

deployment suffersfrom theoreticalflaws


Area patrolling - future

active behavior against intruder


Area patrolling - challenge

reuse pieces of code for different setup


Where do the equations are? I

G. Antonelli and S. Chiaverini.

Kinematic control of platoons of autonomous vehicles.

IEEE Transactions on Robotics, 22(6):1285–1292, Dec. 2006.

G. Antonelli, F. Arrichiello, and S. Chiaverini.

The Null-Space-based Behavioral control for autonomous roboticsystems.

Journal of Intelligent Service Robotics, 1(1):27–39, online March 2007,printed January 2008.

G. Antonelli.

Stability analysis for prioritized closed-loop inverse kinematic algorithmsfor redundant robotic systems.

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


Where do the equations are? II

A. Marino, L. Parker, G. Antonelli, and F. Caccavale.

Fuzzy behavioral control for multi-robot border patrol.

In 17th Mediterranean Conference on Control and Automation,Thessaloniki, GR, June 2009.

A. Marino, L. Parker, G. Antonelli, and F. Caccavale.

Behavioral control for multi-robot perimeter patrol: A finite stateautomata approach.

In Proceedings 2009 IEEE International Conference on Robotics andAutomation, pages 831–836, Kobe, J, May 2009.

A. Marino, L. Parker, G. Antonelli, F. Caccavale, and S. Chiaverini.

A fault-tolerant modular control approach to multi-robot perimeterpatrol.

In 2009 IEEE International Conference on Robotics and Biomimetics(ROBIO 2009), Guilin, PRC, December 2009.


Where do the equations are? III

G. Antonelli, F. Arrichiello, and S. Chiaverini.

The NSB control: a behavior-based approach for multi-robot systems.

Paladyn Journal of Behavioral Robotics, 1(1):48–56, 2010.

G. Antonelli, S. Chiaverini, and A. Marino.

Decentralized deployment with obstacle avoidance for AUVs.

In 18th IFAC World Congress, Milan, I, August 2011.


Acknowledgements in rigorous casual order

Lynne Parker

Fabrizio Caccavale

Antonio Pascoal

Pedro Aguiar

Stefano Chiaverini

Filippo Arrichiello


Patrolling strategies for multiple autonomous vehicles

Giuseppe Casalino, Gianluca Antonelli, Alessandro Marino

Integrated Systems for Marine Enviroment

Ancona - Cassino - Genova - Lecce - Pisa - Trieste - Verona

http://www.isme.unige.it


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