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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
antonelli@unicas.it
http://webuser.unicas.it/lai/robotica
http://www.isme.unige.it/
Marino, Antonelli Karlsruhe, 9 May 2013
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
Marino, Antonelli Karlsruhe, 9 May 2013
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. . .
Marino, Antonelli Karlsruhe, 9 May 2013
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
Marino, Antonelli Karlsruhe, 9 May 2013
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
Marino, Antonelli Karlsruhe, 9 May 2013
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
Marino, Antonelli Karlsruhe, 9 May 2013
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
Marino, Antonelli Karlsruhe, 9 May 2013
Voronoi partitions II
Marino, Antonelli Karlsruhe, 9 May 2013
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
Marino, Antonelli Karlsruhe, 9 May 2013
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
Marino, Antonelli Karlsruhe, 9 May 2013
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.
Marino, Antonelli Karlsruhe, 9 May 2013
Accuracy: example
Global computation of ξ(x) for a given spatial variability τs
τs
x1 x2 x3 x4x
ξ(x)
Marino, Antonelli Karlsruhe, 9 May 2013
Accuracy: example
Computation made by x2 (it does not “see” x4)
τs
x1 x2 x3 x4x
ξ(x)
Marino, Antonelli Karlsruhe, 9 May 2013
Accuracy: example
Only the restriction to V or2 is needed for its movement computation
τs
x1 x2 x3 x4x
ξ(x)
V or2
Marino, Antonelli Karlsruhe, 9 May 2013
Accuracy: example
Merging of all the local restrictions leads to a reasonable approximation
τs
x1 x2 x3 x4x
ξ(x)
V or2
Marino, Antonelli Karlsruhe, 9 May 2013
Accuracy
Based on:
communication bit-rate
computational capability
area dimension
Marino, Antonelli Karlsruhe, 9 May 2013
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
Marino, Antonelli Karlsruhe, 9 May 2013
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
Marino, Antonelli Karlsruhe, 9 May 2013
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
Marino, Antonelli Karlsruhe, 9 May 2013
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
Marino, Antonelli Karlsruhe, 9 May 2013
Experimental validation
joint experiment with Graaltech NURC (NATO Undersea ResearchCenter) facilities, La Spezia, Italy
Marino, Antonelli Karlsruhe, 9 May 2013
Experimental validation
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
Marino, Antonelli Karlsruhe, 9 May 2013
Experimental validation
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
Marino, Antonelli Karlsruhe, 9 May 2013
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
Marino, Antonelli Karlsruhe, 9 May 2013
Conclusions
we missed the sole intruder!
Marino, Antonelli Karlsruhe, 9 May 2013
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
antonelli@unicas.it
http://webuser.unicas.it/lai/robotica
http://www.isme.unige.it/
Marino, Antonelli Karlsruhe, 9 May 2013