coordinated control of autonomous marine vehicles for...

Coordinated control of autonomous marine

vehicles for security applications

Gianluca Antonelli

University of Cassino and Southern Lazio, [email protected]

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

http://www.isme.unige.it

Gianluca Antonelli Rovereto, 12 March 2014

mailto:[email protected]


ISME in brief

Italian Joint Research Unit established in 1999

Sites:

AnconaCassinoGenovaLeccePisaFirenze

Infrastructures from all departements

> 30 researchers (not full time)


ISME research

Main areas:

Underwater robotics

ROVAUVUW manipulationguidance navigation & control

Underwater acoustics

geoacousticsacoustics tomographyimagingsonar

Signal & data processing

geographical informationsystemsdecision support systemsclassification & data fusion


ASVs interception of suspects vessels

Security and surveillance of critical sites within harbors

Teams of Autonomous Surface Vehicles (ASVs) for the optimalthreat intercepts

Threats individuated both by a distributed terrestrial radarsystem and actively by the ASVs


ASVs interception of suspects vessels

Offline Optimization of the ASVs Positioning based on two criteria:

maximization of minimum interception distance

minimization of maximum interception time

Online Selection of the Best ASVs

Selects the ASVs with lowest estimated time to the menace

Takes into account the current traffic


Offline Optimization: interception distance

Maximization of the Minimum Interception Distance

Pa position of the asset ; Pm position of the menace

P vector of all ASVs positions

Pm,i position where the menace m is intercepted by the i-th ASVs

For a specified menace m the best intercepting vehicle is given by

io = argmaxi

(‖Pm,i − Pa‖

)

The worst case: dworst = minPm

[maxi

(‖Pm,i − Pa‖

)]

Optimization of ASV positioning:

P o = argmaxP

dworst = argmaxP

{

minPm

[

maxi

(‖Pm,i − Pa‖

)]}


Offline optimization: interception time

Minimization of the Maximum Interception Time

Any extra ASVs (if N > k) → minimizing the interception time

Let tm,i be the time required for the menace m to be interceptedby the i-th ASV

For a specified menace m the best intercepting vehicle is given by

io = argmini

t(m,i)

The worst case:

tworst = maxPm

[

mini

t(m,i)

]

Optimal positioning:

P o = argminP

tworst = argminP

{

maxPm

[

mini

t(m,i)

]}


Simulations in real scenarios

Red dot: asset; Colored squared: ASV;Smaller circle: dth; Bigger circle: r


Comparison: changing detection radius r

(a) r = 400, dworst = 214, tworst = 22.93, (b) r = 750, dworst = 373, tworst = 22.62


Comparison: changing required min. int. distance dth

(a) dth = 178, dworst = 374, tworst = 20.64, (b) dth = 400, dworst = 460, tworst = 20


Comparison: changing asset’s position


Comparison: changing the number of ASV

(a) 5 USVs (b) 4 USVs


Multi assets simulations

(a) northern asset assumed as target (b) souther asset assumed as target


Current efforts

1.3m long,0.4cm wide

brushless motor

Development of 10 cheap USVs to test theaforementioned algorithms

Rudder+propeller control

Gyro, accelerometers and GPS forlocalization

RF-Modem for communication with basestation

PC-104 and dsPIC as computational power

The setup can be further used to test other high level algorithms:adaptive sampling, coordination algorithms, etc.


Multi-robot harbor patrolling

Problem formulation

Totally decentralized

Robust to a wide range of failures

communicationsvehicle lossvehicle still

Flexible/scalable to the number of vehicles add vehicles anytimePossibility to tailor wrt communication capabilities

Not optimal but benchmarking required

Anonymity

To be implemented on a real set-up obstacles. . .


Proposed solution

Proper merge of the Voronoi and Gaussian processes concepts

Motion computed to increase information

Framework to handle

Spatial variability regions with different interestTime-dependency forgetting factorAsynchronous spot visiting demand

Mathematically strong overlap with (time varying) coverage,deployment, resource allocation, sampling, exploration, monitoring, etc.slight differences depending on assumptions and objectivefunctions


Voronoi partitions

Voronoi partitions (tessellations/diagrams)

Subdivisions of a set S characterized by a metric with respect to afinite number of points belonging to the set

Union of the cells gives back thesetThe intersection of the cells isnullComputation of the cells is adecentralized algorithm withoutcommunication needed


Voronoi partitions


Background I

Variable of interest is a Gaussian processhow much do I trust that

a given point is safe?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

{

µ = µ(xp, t) + c(xp, t)TΣ−1

Sa(ya − µa)

σ = 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


Description

The variable to be sampled is a confidence map

Reducing the uncertainty means increasing the highlighted term

µ = µ(xp, t) + c(xp, t)TΣ−1

Sa(ya − µa)

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

Sac(xp, t)︸︷︷︸

ξ

− > ξ example


Description

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

Compute the optimal motion to reduce uncertainty

Several choices possible:minimum, minimum over anintegrated path, etc.


Accuracy: example

Global computation of ξ(x) for a given spatial variability τs

τs

x1 x2 x3 x4x

ξ(x)


Accuracy: example

Computation made by x2 (it does not “see” x4)

τs

x1 x2 x3 x4x

ξ(x)


Accuracy: example

Only the restriction to V or2 is needed for its movement computation

τs

x1 x2 x3 x4x

ξ(x)

V or2


Accuracy: example

Merging of all the local restrictions leads to a reasonable approximation

τs

x1 x2 x3 x4x

ξ(x)

V or2


Accuracy

Based on:

communication bit-rate

computational capability

area dimension


Numerical validation

Dozens of numerical simulations by changing the key parameters:

vehicles number

faults

obstacles

sensor noise

area shape/dimension

comm. bit-rate

space scale

time scale

2

3 4


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


Some benchmarking

Comparison between different approaches

00

LawnmowerProposedRandomDeployment0.5

1.5

2

200 400 600 800 1000 1200

1

[]

step

same parameterslawnmower rigidwrt vehicle lossdeployment suffersfrom theoreticalflaws


Experimental validation with ASVs

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


Experimental validation with ASVs

3 Medusas

switched off only forlow battery

obstacle

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


Experimental validation with AUVs

Vehicle characteristicsinternal diameter .125mexternal diameter .14mlength 2mmass 30 kgmass variation range .5 kg(at water density 1.031 kg/m3)moving mass max displacement 0.050mLead acid batteries 12V 72Ahautonomy at full propulsion 8 hdiving scope 0–50 mbreak point in depth 100mspeed with jet pump propeller 1.01m/s 2 knotsspeed with blade propeller 2.02m/s 4 knotscpu 1GHz, VIA EDENdram 1GB, DDR2



joint experiment with Graaltech NURC (NATO Undersea ResearchCenter) facilities, La Spezia, Italy



2 Folaga, 4 acoustic transponders, 1 gateway buoy

110× 80× 4m

1.5m/s

33 minutes

WHOI micromodem 80 bps

Time Division Multiple Access

localization: every 8 suser comm: 31 byte/min with 14 s delay



Due to poor communication, the algorithm runs by predicting themovement of the other

# fields size (bytes)

1) vehicle ID 2

2) localization time 4

3) vehicle latitude 4

4) vehicle longitude 4

5) vehicle depth 4

6) target latitude 4

7) target longitude 4

8) target depth 4


Experimental validation with AUVs - video

Coverage index

200 400 600 800 1000 1200 1400 1600

0.1

0.2

0.3

0.4

[]0.5

00

time [s] 1800


Conclusions

we missed the sole intruder!


Acknowledgements in rigorous casual order

Alessandro Marino

Pino Casalino

Filippo Arrichiello

Sandro Torelli

Alessio Turetta

Enrico Simetti

Stefano Chiaverini Alessandro Sperinde


Coordinated control of autonomous marine

vehicles for security applications

Gianluca Antonelli

University of Cassino and Southern Lazio, [email protected]

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



mailto:[email protected]


coordinated control of autonomous marine vehicles for...

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