coordinated control of autonomous marine vehicles for...
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Coordinated control of autonomous marine
vehicles for security applications
Gianluca Antonelli
University of Cassino and Southern Lazio, Italyantonelli@unicas.it
http://webuser.unicas.it/lai/robotica
http://www.isme.unige.it
Gianluca Antonelli Rovereto, 12 March 2014
ISME in brief
Italian Joint Research Unit established in 1999
Sites:
AnconaCassinoGenovaLeccePisaFirenze
Infrastructures from all departements
> 30 researchers (not full time)
Gianluca Antonelli Rovereto, 12 March 2014
ISME research
Main areas:
Underwater robotics
ROVAUVUW manipulationguidance navigation & control
Underwater acoustics
geoacousticsacoustics tomographyimagingsonar
Signal & data processing
geographical informationsystemsdecision support systemsclassification & data fusion
Gianluca Antonelli Rovereto, 12 March 2014
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
Gianluca Antonelli Rovereto, 12 March 2014
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
Gianluca Antonelli Rovereto, 12 March 2014
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‖
)]}
Gianluca Antonelli Rovereto, 12 March 2014
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)
]}
Gianluca Antonelli Rovereto, 12 March 2014
Simulations in real scenarios
Red dot: asset; Colored squared: ASV;Smaller circle: dth; Bigger circle: r
Gianluca Antonelli Rovereto, 12 March 2014
Simulations in real scenarios
Red dot: asset; Colored squared: ASV;Smaller circle: dth; Bigger circle: r
Gianluca Antonelli Rovereto, 12 March 2014
Comparison: changing detection radius r
(a) r = 400, dworst = 214, tworst = 22.93, (b) r = 750, dworst = 373, tworst = 22.62
Gianluca Antonelli Rovereto, 12 March 2014
Comparison: changing required min. int. distance dth
(a) dth = 178, dworst = 374, tworst = 20.64, (b) dth = 400, dworst = 460, tworst = 20
Gianluca Antonelli Rovereto, 12 March 2014
Comparison: changing asset’s position
Gianluca Antonelli Rovereto, 12 March 2014
Comparison: changing the number of ASV
(a) 5 USVs (b) 4 USVs
Gianluca Antonelli Rovereto, 12 March 2014
Multi assets simulations
(a) northern asset assumed as target (b) souther asset assumed as target
Gianluca Antonelli Rovereto, 12 March 2014
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.
Gianluca Antonelli Rovereto, 12 March 2014
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. . .
Gianluca Antonelli Rovereto, 12 March 2014
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
Gianluca Antonelli Rovereto, 12 March 2014
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
Gianluca Antonelli Rovereto, 12 March 2014
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
Gianluca Antonelli Rovereto, 12 March 2014
Voronoi partitions
Gianluca Antonelli Rovereto, 12 March 2014
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
Gianluca Antonelli Rovereto, 12 March 2014
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
Gianluca Antonelli Rovereto, 12 March 2014
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.
Gianluca Antonelli Rovereto, 12 March 2014
Accuracy: example
Global computation of ξ(x) for a given spatial variability τs
τs
x1 x2 x3 x4x
ξ(x)
Gianluca Antonelli Rovereto, 12 March 2014
Accuracy: example
Computation made by x2 (it does not “see” x4)
τs
x1 x2 x3 x4x
ξ(x)
Gianluca Antonelli Rovereto, 12 March 2014
Accuracy: example
Only the restriction to V or2 is needed for its movement computation
τs
x1 x2 x3 x4x
ξ(x)
V or2
Gianluca Antonelli Rovereto, 12 March 2014
Accuracy: example
Merging of all the local restrictions leads to a reasonable approximation
τs
x1 x2 x3 x4x
ξ(x)
V or2
Gianluca Antonelli Rovereto, 12 March 2014
Accuracy
Based on:
communication bit-rate
computational capability
area dimension
Gianluca Antonelli Rovereto, 12 March 2014
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
Gianluca Antonelli Rovereto, 12 March 2014
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
Gianluca Antonelli Rovereto, 12 March 2014
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
Gianluca Antonelli Rovereto, 12 March 2014
Experimental validation with ASVs
Laboratory of Robotics and Systems in Engineering and ScienceIST, Technical University of Lisbon
Gianluca Antonelli Rovereto, 12 March 2014
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
Gianluca Antonelli Rovereto, 12 March 2014
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
Gianluca Antonelli Rovereto, 12 March 2014
Experimental validation with AUVs
joint experiment with Graaltech NURC (NATO Undersea ResearchCenter) facilities, La Spezia, Italy
Gianluca Antonelli Rovereto, 12 March 2014
Experimental validation with AUVs
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
Gianluca Antonelli Rovereto, 12 March 2014
Experimental validation with AUVs
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
Gianluca Antonelli Rovereto, 12 March 2014
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
Gianluca Antonelli Rovereto, 12 March 2014
Conclusions
we missed the sole intruder!
Gianluca Antonelli Rovereto, 12 March 2014
Acknowledgements in rigorous casual order
Alessandro Marino
Pino Casalino
Filippo Arrichiello
Sandro Torelli
Alessio Turetta
Enrico Simetti
Stefano Chiaverini Alessandro Sperinde
Gianluca Antonelli Rovereto, 12 March 2014
Coordinated control of autonomous marine
vehicles for security applications
Gianluca Antonelli
University of Cassino and Southern Lazio, Italyantonelli@unicas.it
http://webuser.unicas.it/lai/robotica
http://www.isme.unige.it
Gianluca Antonelli Rovereto, 12 March 2014
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