Download - ICRA 2013 talk 2

Experimental Results of Coordinated Coverage byAutonomous Underwater Vehicles

Alessandro Marino, Gianluca Antonelli

Universita di Salerno, ItalyUniversita di Cassino & ISME (Integrated Systems for Marine Environment), Italy

[email protected]

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

http://www.isme.unige.it/

Marino, Antonelli Karlsruhe, 9 May 2013

mailto:[email protected]


http://www.isme.unige.it

CO3AUVs

Cooperative Cognitive Control of Autonomous Underwater Vehicles

fundings : European FP7, Cognitive Systems, Interaction, Roboticskind : Collaborative Project (STREP)duration : 3 years, 2009-2012partners : Jacobs University, DE;

ISME, I;Instituto Superior Tecnico, P;GraalTech, I

http://www.Co3-AUVs.eu


Problem formulation

Multi-robot harbor patrolling

Totally decentralized

Robust to a wide range of failures

communicationsvehicle lossvehicle still

Flexible/scalable to the number of vehicles add vehicles anytime

Possibility 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 objective functions


Background

theoretical details

Antonelli, Chiaverini, Marino, A coordination strategy for multi-robot

sampling of dynamic fields , ICRA 2012

experimental validation with surface vehicles

Marino, Antonelli, Aguiar, Pascoal, Multi-robot harbor patrolling: a

probabilistic approach, IROS 2012


Voronoi partitions I

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 the set

the intersection of the cells is null

computation of the cells is a

decentralized algorithm without

communication needed


Voronoi partitions II


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 I

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 II

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 an

integrated 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 parameters

lawnmower rigid wrtvehicle loss

deployment suffersfrom theoreticalflaws


Vehicle characteristics

internal 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


Experimental validation

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 - 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!


Experimental Results of Coordinated Coverage byAutonomous Underwater Vehicles

Alessandro Marino, Gianluca Antonelli

Universita di Salerno, ItalyUniversita di Cassino & ISME (Integrated Systems for Marine Environment), Italy

[email protected]


http://www.isme.unige.it/


mailto:[email protected]


http://www.isme.unige.it

Download - ICRA 2013 talk 2

Top Related