cooperative auv 2
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localization and environmentallocalization and environmental
sampling by AUV teamssampling by AUV teamsAndrea Caiti (1,2), Pino Casalino (1,3), Andrea Munaf (1,2),
Enrico Simetti (1,3), Alessio Turetta (1,3)
Andrea Caiti (1,2)
, Pino Casalino (1,3), Andrea Munaf (1,2),
Enrico Simetti (1,3), Alessio Turetta (1,3)
(1) ISME Interuniv. Res. Ctr. Integrated Sys. Marine Env.
(2) DSEA & Centro Piaggio - University of Pisa, Italy
(1) ISME Interuniv. Res. Ctr. Integrated Sys. Marine Env.
(2) DSEA & Centro Piaggio - University of Pisa, Italy
,
caiti@dsea.unipi.it
,
caiti@dsea.unipi.it
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MenuMenu
AUVs for environmental sampling
propelled
hybrid
Adaptive sampling and autonomydata-driven vs. model-driven
Limitations to uw robot team cooperationcommunication
localization
Adaptive sampling in a communication
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replace ship and/or buoys with autonomous vehicles
pre-program the mission, receive data at a remote station
CTDs, water quality, bathymetry, seabed morphology
data quality enhanced by the use of an underwater platform
New measurements possibilities
gradient-following, feature mapping, synoptic measurements
exploit the on-board intelligence as data are gathered:
adaptivity
ex loit the availabilit of multi le vehicles:
team cooperation
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Some AUVs Some AUVs ceanograp c g ers
very long endurance
inexpensive Hybrid vehiclesoceanograp c samp ng
pre-programmed missioncooperation (Leonard et al.)
inexpensiveoceanographic sampling
-Propelled AUVs
deep waterlong endurance
cooperation
very expensive
seabed mapping
pre-programmed mission
shallow watersome endurance
moderately expensive
oceanography, seabed mapping
pre-programmed, cooperation
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and some ASV - and some ASV -
Autonomous Surface VehiclesAutonomous Surface Vehicles
OASIS (NOAA) - Delfim (IST)
Scout (MIT) - Charlie (CNR-
ISSIA)
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Adaptation and autonomyAdaptation and autonomy
Sam le at oints/ aths that maximize some fi ure of
merit related to our knowledge of the sampled field
In the oceanographic context adaptation can be:
Model driven: use model rediction to determine next mission no autonomous planning needed (but autonomous navigation
required)
open loop - feedback
Data driven: use data to determine next way-point
autonomous lannin as well as autonomous navi ation
feedback only
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Where/when is data drivenWhere/when is data driven
Whenever occurrence of anomalous
events as to e mon tore , etecte ,
classified
. . Fixed sensors in key positions
Periodic sampling around the field
Other instances: water quality in touristic
industry discharge
security: intrusion detection,
m ne or ea searc
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Some formal frameworksSome formal frameworksfor data-driven adaptive samplingfor data-driven adaptive sampling
increase sample density when the measured quantity is rapidly
varying (either in time and/or in space)
estimate variation rate from past or neighborhood data
deterministic setting: smoothness
probabilistic setting: information gain
app cat on- epen ent we g ng
use tools from both approaches
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Deterministic setting: smoothnessDeterministic setting: smoothness
A measure of the variability of the field
weighed sum of the time and spatial derivativesn
F t b D F c D F = +r , , , ,n
( ; )nS
F t kr
projection over a space of basis functions, ordered with
increasing variability ( ; ) ( ; ) ;i i i iiF t h t h F
= =r r p f2
ii nh k
=
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Probabilistic setting: information gainProbabilistic setting: information gain
Minimize the expected uncertainty of the field
A priori mean and covariance of the field( ; ) { ( ; ; )}F t E F t =r r
m measurements at points (ri ;ti), estimation algorithm
( , '; , ') {[ ( ; ) ( ; )][ ( '; ') ( '; ')]}C t t E F t F t F t F t = r r r r r r
choose the m samples so to minimize the a posteriori
( )( ; ) ( ; ), ( ; ) ; 1,..., )i iF t T F t F t i m= =r r r
uncer a n y o e es ma e e
{[ ( ; ) ( ; )][ ( ; ) ( ; )]}J E F t F t F t F t d dt= r r r r r
(adapted from Leonard et al., 07)
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Sensors
deployment
ommun cat on
& fusionSense
e erm ne
next position
0
centralized
;i ir
each
nodejointly
str ute
collaborative
( ; )i iF tr ( ; ) ( ( ; ) )1,..., 0,...
i iF t T F t i k
== =
r r ;i itr
(adapted from Huguenin & Rendas, 06)
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Model-driven predictor/corrector methodsModel-driven predictor/corrector methods
Sensor sam linKalman-like
Process Modelposition
estimation
static
ada tiveProcess( / 1)
/ 1
k k
k k
x
C
sampling
Compare &
Estimate
predictioncorrection
Optimize expected
( / )k kC
information gain
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Cooperation and coordination
ada ted from Whitcomb and Yuh 09
Cooperation and coordination
ada ted from Whitcomb and Yuh 09
Team of heterogeneous vehicles
Payoff ada tation
same/better performance
no single point weakness
sensor networks
ubiquitous computing
mo e agents, consensus, ...
Drawback:coo eration and coordination re uire inter-
vehicle communications
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The acoustic communication channelThe acoustic communication channel Transmission loss
limitations on channel capacity / requirements on source
level
Multi-path structure causes symbolic interference
limitations on codin /decodin schemes
Acoustic channel predictions used to determine
relativedepths and maximumrange among Tx/Rx to
guarantee a given SNR
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Predicting channel characteristicsPredicting channel characteristics
--
SNR(x,w) = SL - TL(x,w) - N(w) Numerical Code
Equations for channel characterization
environmental conditions
Includes ( in TL):
[Bandwidth]
intrinsic attenuation
wave interference patterns
apac y
[TX Power]
range independent
stationary sources
(Stojanovic, 07) (Caiti et al., 09)
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Limitations & opportunities forLimitations & opportunities forcooperative robotscooperative robots
Network connectivity constraints limit therelative mobilit of the a ents
Mobility of the agents can be exploited todynamically maximize some figure of merit of the
communication channel
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What happens with COTSWhat happens with COTS
COTS acoustic modems have fixed bit rate and source
level, with BER depending on the SNR
Bit rate: from 256 b/s to optimistic 15 kb/s
of the individual agents and on parsimonious comms
overhead
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Team localizationTeam localization
A team of AUVs must be cheap!
Underwater navigation is a major source of vehicle cost inertial systems
Use the acoustic modems also to localize acoustically
measurements
Some vehicles at the surface eo-referenced relativelocalization
DGPS
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Cooperative uw localizationCooperative uw localization
A hot research topics J. Leonard; Sukhatme; Chitre; Antonelli ...
Alternate between ranging and communication
, , ...
Use depth sensors to convert from 3D to 2D
Several issues: linear vs. nonlinear estimate
observability depending on relative vehicle motion
on-board correction for bended ray-paths
clock synchronization
Our approach: distributed EKF with delay (from team
theory work dating back to the 80s!)
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Designing behavioursDesigning behaviours
Not observable Observable EKF Error
(Antonelli, Caiti et al., ICRA 10)
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Real-time Ray-tracing (RT2)Real-time Ray-tracing (RT2)
Compensate ray bending through a look-up table
en ng ray error: ncreas ng mpor ance w range
In distributed localization range errors will accumulate
- .
PTT:Partial
Travelled
PTD:
Distances
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Look-up table and on-line estimateLook-up table and on-line estimate
receiver depth
V1 1
V1 2
. .
.
V1 n
V V . . V2 1 2 2 . 2 n
. . . . . .
. .
.
. .
.em
itter
de
pth
Vn 1
Vn 2
. .
.Vn n
])(,),(),([ 21 mabababab kVkVkVV L=
k
rrrrRTkV ababababababababab ],,,,|,,,,[)(43214321 = (Casalino et al.,
Oceans 10)
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Cooperative adaptive sampling withCooperative adaptive sampling with
- Each vehicle- Each vehicle
per orms s own
vertical sampling
per orms s own
vertical sampling
- All the vehicles
share information
- All the vehicles
share information
(acoustic)(acoustic)
plan the next move of the team in order to optimize:plan the next move of the team in order to optimize:
- sampling map quality (map res. below a given threshold)- overall area coverage measurements (cover the area by sampling
where needed)
- sampling map quality (map res. below a given threshold)- overall area coverage measurements (cover the area by sampling
where needed)
- while maintaining connectivity
range constraint among the vehicles, varying in space and time
- while maintaining connectivity
range constraint among the vehicles, varying in space and time
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deterministic data-driven approach
deterministic data-driven approach
Each vehicle computes its next admissible range for its next
measurement (admissible exploring radius) on the basis of the
Each vehicle computes its next admissible range for its next
measurement (admissible exploring radius) on the basis of the
local smoothness of the environmental map -Local computationslocal smoothness of the environmental map -Local computations
( ) ( ; ) ( ; )kF t h t
=r r : RBF family( ) ( )( ; ) ( ; ) ( ; ) ( )k kI IF t F t F t G h
r r rh: fill distance
G: known function
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Optimize area coverageOptimize area coverage
Local decision rule based: maximize distance rom reviousLocal decision rule based: maximize distance rom revious
samples and from next location of the other vehiclessamples and from next location of the other vehicles
Information exchange needed
Information exchange needed
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Communication/range constraintCommunication/range constraint
Next sampling points must preserve connectivity of the
communication structure
Next sampling points must preserve connectivity of the
communication structure
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Serial graph structure, fixed topologySerial graph structure, fixed topology
Use of
Dynamic CTransition Cost
Programming
j
Possible local moves Next
graph
)pp(C 1jj +,
amp ng c rc es
(independently locally evaluated)
Backward phase Forward phase
Distributed implementation of dynamic programming!
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Articulated chain structure,
d i l
Articulated chain structure,
d i lMinimum
algorithm
current graphnext graph
Compare with
d.p. solution
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Dynamic programming on test dataDynamic programming on test data
3.5
4
4.5
5
38.4
38.5
38.6salinity (ppm)
4
4.5
5
38.4
38.6salinty
(ppm)
1.5
2
2.5
3
y(km
)
37.9
38
38.1
38.2
38.3
2
2.5
3
3.5
y(km)
38
38.2
0 1 2 3 4 5
0
0.5
1
x (km)
37.6
37.7
37.8
0
0.5
1
1.5
37.6
37.8
0 1 2 3 4 5
x (km)
Test data,
courtesy
A. Alvarez,
Reconstructed
Via Cooperative Sampling
NURC an nterpo at on
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Graph theory based cooperationGraph theory based cooperation
Sound optimality proof
s r u e mp emen a on o cen ra ze a gor ms
Heavy comms overload
increase
Possibility of a truly local rule-based algorithm?
Yes behaviours
Different approaches and implementations nosystematic design rule in general, some cases analyzed
Not always optimality guaranteed
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An example of cooperative algorithmAn example of cooperative algorithm
in security applicationin security application Goals
Critical asset protection
Maintaining acoustic connectivity among the team
Each agent/node
Builds a local map of channel characteristics and comms
performance
Updates the map when new environmental measurements
Adapts its behaviour to tackle changes in the environment
u e- ase e av our an po en a e s
(Caiti et al., 2009/10)
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AUVs e ui ed with: Acoustic modem max range:RC
Detection sonar max range:RD
cover with the sonarsminx a ix x the greatest areaaround the asset to
rotect2
, ,i ji j D Dx x R R i j
+
2, :ii j Ci j x x R
closest neighborhood information
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--
Rule 1: ove toward the asset
Rule 2:Move away from your closest neighbor
Implemented through gradients of artificial potential
- ,
Vehicle course: vector sum of the two contributions
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Ineherently robust to communication loss &
equipment failure!
Can include sonar/modem directionality
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With omnidirectional sonar/modems infinite solution
exists (all symmetric configurations around the asset)
The rule-based algorithm stabilizes around one solution
,
where they are, or move keeping the symmetry around
the asset and s annin the whole set of solutions
We have never seen the symmetric motion in
simulations; in practice it can be ruled out (note: the
symmetric motion may be a plus, not a minus!)
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Some more commentsSome more comments
Small comms overheadEach vehicle communicates with its closest neighbor
Data to be TX:
Agent Position Maximum Detection Sonar Range
Built in Emergency Procedure
If an a ent loses comms oes to the asset
Distributed, scalable algorithm, independent from AUV #
Comms delay do not alter result, but imply longer vehicle
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Experimental testExperimental test
6-30 September 10, Pianosa Island
Networked communicationCooperative localization
Secur ty e av our
2 vehicles
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ConclusionsConclusions
A set of tools for autonomous cooperative adaptive
context: data-driven adaptation
deterministic and probabilistic metrics
acoustic communication prediction
cooperative distributed localization
graph-theoretic approach
guaranteed optimality
ess ex e, commun ca on n ens ve
behaviour-basedrobust
g comms
optimality and convergence not guaranteed but for special cases
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