LOCALIZATION IN AN AUTONOMOUS UNDERWATER

MULTI-ROBOT SYSTEM DESIGNED FOR COASTAL AREA

MONITORING

Zhongliang Hu, Eemeli Aro, Tapani Stipa†, Mika Vainio and Aarne Halme

Finnish Center of Excellence in Generic Intelligent Machines Research, Aalto University School of Science and Technology

P.O. Box 15500, FI-00076 Aalto, Finland

†Marine Hydrodynamics, Finnish Meteorological Institute, Erik Palmenin aukio 1, P.O. Box 50, FI-00101, Helsinki, Finland

Keywords:

Multi-robotic system, Autonomous underwater localization, Underwater acoustic communication, Coastal

area monitoring.

Abstract:

In this paper we present an multi-robotic system for underwater exploration, speciﬁcally for coastal seas.

The novelty of this system is its enhanced performance in underwater localization using underwater acoustic

ranging and data transfer between the ﬂoats. This process is not dependent on any ﬁxed infrastructure, which is

usually a requirement for such missions. An algorithm is implemented in the ﬂoats for underwater localization,

based on an Extended Kalman Filter. Extensive real scenario simulation test results are presented.

1 INTRODUCTION

The value of services provided by the coastal seas, in-

cluding estuaries, to the human welfare is estimated

to be higher than those of terrestrial or open ocean

systems (Costanza et al., 1998). The relevant spatial

and seasonal scales for biological variability are of-

ten related to hydrophysical events; these are mostly

unpredictable and practically impossible to cover by

traditional monitoring with sparse sampling.

Autonomous underwater multi-probe system for

coastal area / shallow water monitoring (SWARM)

was an EU-funded (FP5, 2003-2005) project aiming

to design, implement and test a multi-robot system

that could measure local and transient biological and

physical variability in the Baltic Sea and similar areas

at the scale relevant for single events.

Based on the initial ideas and hardware designed

and built during the SWARM project, the Finnish

Center of Excellence in Generic Intelligent Machines

(GIM) has continued the related research. As one of

the results, a localization method has been developed

that requires no additional infrastructure, ﬁxed bea-

cons or expensive inertial units, while producing ac-

curate real-time location estimates for the members

of the system. The method is based solely on un-

derwater inter-robot acoustic communication and oc-

casional GPS ﬁxes while on the surface. It is worth

mentioning that the internal intelligence which is es-

sential for the units to navigate and survive in the dy-

namic and hostile underwater environment makes the

units stand out from mere buoyancy ﬂoats to intelli-

gent underwater robots.

In this paper, the main features of this system

working in an extremely challenging environment

will be ﬁrst brieﬂy introduced. Then, the main topic

of this paper, i.e., a novel localization approach will

be presented and veriﬁed with suitable tests. Finally,

some conclusions are drawn and some information re-

garding the future work will be presented.

2 SYSTEM DESCRIPTION

The scientiﬁc objective of the SWARM project was

to design, implement and test a novel highly redun-

dant underwater monitoring system for shallow water

areas (Vainio et al., 2004). The system consists of

multiple homogenous, robust and easy to use under-

water robots (autonomous intelligent proﬁling ﬂoats)

that can perform two week missions autonomously.

The ﬂoats control their buoyancy but move otherwise

freely with the water ﬂows. They communicate with

the control station via Iridium satellite communica-

tion and use inter-robot acoustic ranging and commu-

nication for localization and data exchange. In addi-

tion to measuring the standard variables (pressure,

232

Hu Z., Aro E., Stipa T., Vainio M. and Halme A. (2010).

LOCALIZATION IN AN AUTONOMOUS UNDERWATER MULTI-ROBOT SYSTEM DESIGNED FOR COASTAL AREA MONITORING.

In Proceedings of the 7th International Conference on Informatics in Control, Automation and Robotics, pages 232-239

DOI: 10.5220/0002945502320239

 SciTePress

temperature, conductivity), the system observes cer-

tain algal groups with a novel ﬂuorometer.

Figure 1: The general description of the SWARM system.

The diving proﬁle of the ﬂoats was selected based

on two main issues: to enable the system to measure

the key environmental parameters from the main ar-

eas of interest and to support the proposed localiza-

tion method. It also matches the standard proﬁle used

by Argo ﬂoats globally, but of course with shallower

depths (Davis et al., 2001).

Figure 2: Diving proﬁle of a unit.

T1: Waiting time. The ﬂoat spends most of its

time waiting on this level. The beneﬁt of this is such

that the ﬂoat does not need to stay on the surface all

the time but by taking advantage of communication it

can still keep a track of its location during the mission.

T2: Diving time. When T1 expires, the ﬂoat will

start to dive to a deeper level and wait there for the

start of proﬁling.

T3: Proﬁling time. After T2 expires, the

ﬂoats ascents to surface, during which time it takes

CTD(conductivity, temperature and density) proﬁle.

When the ﬂoat reaches the surface, it gets a GPS

ﬁx and contact the operator station for possible com-

mands and reports data.

3 EXPERIMENTAL SETUP

During the EU-funded phase, the functionality of

each of the subsystems were tested separately before

ﬁnal integration into a prototype. Major part of these

tests were carried out in Gulf of Finland on the south

coast of Finland. In addition to the real system, a

MATLAB based simulator was designed at an early

phase of the project mainly for the testing of local-

ization algorithms. After the end of the EU funding

a more sophisticated simulator was constructed and

used for the testing presented in this paper.

Figure 3: Subsystems shown in a block diagram.

A simpliﬁed block diagram shown in Figure 3

illustrates that the developed robotic prototypes are

full-blooded autonomous proﬁling ﬂoats which are

equipped with extensive repertoire of environmental

sensors. Furthermore, they possess certain level of in-

telligence, which is required to survive autonomously

in a dynamic and hostile coastal waters.

3.1 Physical Realization

The main physical output of the SWARM project was

four 3

generation functional ﬂoats ready for open

sea testing. These ﬂoats have since been extensively

modiﬁed and tested in the lab. Full-scale tests are ex-

pected to start from summer 2010. See Figure 4.

3.2 Simulator

A new simulator is implemented as an attempt to pro-

duce a near-realistic simulated environment for au-

tonomous ﬂoats. The simulator uses a server-client

architecture, with the environment as a server to

which each ﬂoat connects. Specialized clients may

also connect to the environment to act as ground sta-

tions or data display/logging agents. Time in the sim-

ulator advances at a multiple of real time. The in-

terfaces available to a program acting as a simulated

ﬂoat are modeled to match those of an actual ﬂoat,

including simulated sensor errors and measurement

LOCALIZATION IN AN AUTONOMOUS UNDERWATER MULTI-ROBOT SYSTEM DESIGNED FOR COASTAL

AREA MONITORING

233

Figure 4: Available physical ﬂoats (own by SWARM con-

sortium). Floats are around 2 m tall and weigh approxi-

mately 40 kg.

time delays. This allows for hardware-in-the-loop

testing using the real ﬂoat hardware, with each sen-

sor connected using a serial cable to an external com-

puter running an interface program connected to the

simulated environment.

Figure 5: Snapshot of the developed simulator.

The simulator components use GIMnet (Saarinen

et al., 2007) to communicate, allowing each program

to run on a separate networked computer if required.

The architecture is designed to scale, and can simul-

taneously model hundreds of ﬂoats at a simulation

speed far in excess of real time. The simulator can

make use of data from anywhere in the world, pro-

vided that bathymetry and three-dimensional time-

variant salinity, temperature and current vector infor-

mation is available to some precision; intermediary

values will be interpolated. The data used for the tests

presented here is from the BalEco ecosystem model

of the Finnish Institute of Marine Research (currently

Finnish Meteorological Institute).

4 FLOAT LOCALIZATION

The core element of a SWARM unit positioning sys-

tem is a GPS receiver, which is included in each of

the robots. When on the surface, a unit gets its posi-

tion from the satellites. The frequency of these GPS

ﬁxes must be considered carefully. They will natu-

rally always bring much needed fresh and accurate

information to the system, but on the other hand, they

force the units to come to the surface which consumes

energy and increase the possibility of being hit by a

passing boat. In any case, after the GPS ﬁx, the inter-

robot acoustic communication system between robots

is then utilized in positioning the underwater units.

It is essential to relate the measurement data that

the ﬂoats have gathered underwater with the geo-

graphical coordinates where they have been mea-

sured. A conventional ﬂoat proﬁling operation, as

shown in Figure 2, givesreasonable results for a series

of measurement when the ﬂoat comes back to the sur-

face from the deep depth, since such surfacing hap-

pens in a quite short time and the currents affecting

this movement are negligible. However, this move-

ment only gives a vertical series measurements at a

given location. We are more interested in the sea con-

ditions in a larger area, including prevailing sea cur-

rents. One approach for this can be found in (McFar-

land and Honary, 2003).

4.1 Baltic Sea

Shallow seas pose speciﬁc problems for robotic ocean

instruments. They are often strongly stratiﬁed, turbid

and have vibrant maritime activities. The bottom is

soft, to the extent that it may have adhesive powers on

instruments that land on the bottom. Figure 6 shows

an example of the complex vertical structure of tem-

perature, salinity and ﬂuorescence, with almost unno-

ticeable physical features that nevertheless have their

impact on the biological situation.

The spatial scales of motion are often small in the

horizontal direction as well. Therefore, the state of

motion in one place is not a very good predictor of

the state some kilometers away. These factors make

it harder to observe shallow seas with robotic instru-

ments than the ocean. There should be a large number

of robotic instruments to capture the spatial variabil-

ity, yet the loss rate of robotic instruments in shallow

seas is expected to be signiﬁcantly larger than in the

blue oceans, where a typical Argo ﬂoat may survive

3-5 years.

ICINCO 2010 - 7th International Conference on Informatics in Control, Automation and Robotics

234

Figure 6: Complex vertical structure of temperature, salin-

ity and ﬂuorescence against depth in the Baltic Sea.

4.2 Acoustic Communication

The range and reliability of underwater acoustic com-

munication is affected by a number of factors, in-

cluding signal attenuation, noise, reverberation and

sound channeling. A transmitted signal will be re-

ﬂected from the surface or seabed and interfere with

the direct path signal. Variations in the temperature

and salinity at different depths will result in vari-

ations in the speed of sound, which will inﬂuence

the path of the signal. The sound speed proﬁle for

the Baltic Sea is in fact conducive to the formation

of a sound channel, a depth at which acoustic sig-

nals will reﬂect internally due to a minimum in the

speed of sound. This channel is present mostly dur-

ing the summer, and varies in depth from 20m to 80m.

Using this sound channel, ﬂoats in the Baltic Sea

can theoretically achieve communications and rang-

ing over distances of up to 10km. In addition to com-

munication, acoustic transmissions may also be used

for ranging, by tracking the time of ﬂight of mes-

sages. The SWARM ﬂoats implement a ping-pong

type scheme for measuring the round-trip travel time

between ﬂoats.

In this paper we use a simple linear model of the

probability of construction a communication between

drifters. When two ﬂoats are at immediate vicinity

they have a near guaranteed communication probabil-

ity, whereas at around 6 kilometers distance, the prob-

ability of having a communication channel between

two ﬂoats falls almost to zero. To determine the posi-

tion of oneﬂoat from another, a ranging signal is used.

In effect, the ﬂoat transmits a message and waits for

the reply from the receiving ﬂoat.

A unit that has been on the surface sends its newly

obtained GPS position information through acoustic

modem once it has reached the deﬁned communica-

tion depth and it is its time to communicate. Knowing

their own estimated positions and past trajectories, the

underwater units can reduce the uncertainty of their

position based on this information. A positional accu-

racy in the scale of hundreds of meters is acceptable

in this application. The biggest advantage of this po-

sitioning system is the fact, that there is no need for

any additional positioning devices, e.g. ﬁxed acous-

tic buoys used in (Akyildiz et al., 2006; Dario et al.,

2005).

4.3 Algorithms and Implementations

The Kalman ﬁlter is an efﬁcient and robust recursive

ﬁlter that estimates the internal state of a dynamic sys-

tem from a series of noisy measurements. It can also

estimate the variables which can’t be directly mea-

sured (Welch and Bishop, 1995). In our case, this

corresponds to the underwater sea current.

For this paper, the underwater localization estima-

tion is based on an Extended Kalman Filter(EKF).

The state variables are geographical positions of the

units and the vectors describing the currents in the

sea in different depth layers, while GPS ﬁxes and dis-

tances between the ﬂoats through an acoustic ranging

being the measurements. Note that in this particu-

lar case, we do not include the depth coordinate into

the estimation as well as the vertical part of the sea

current vector because the measurement of the depth

itself is accurate enough. Since we can have a rela-

tively good measurement of the depth from the CTD

sensor, and taking the system propulsion into account

is not beneﬁcial. This arrangement will also reduce

the complexity of the computation which is a limiting

factor in the real system onboard.

First we start with deﬁning the state and measure-

ment vectors. The current vector at level j at position

(x,y) at k time step of EKF:

(x,y)(k) = [ f

(k) f

(k)]

(1)

In Section 4.1 we have described the basic situa-

tion for the Baltic Sea current and its unique charac-

ter of being layered. Thus, the environment can be

thought as series of layers that each have a ﬂow vec-

tor representing the sea currents, which slowly varies

from one location to another. The units are located at

these layers as shown in Figure 7.

To better estimate such phenomena, we divided

the sea current vectors into three layers. In addition to

limit the computation complexity, we also choose this

number to correspond to the diving proﬁle as shown

in Figure 2. The ﬂow vector at position (x,y) at k time

step of EKF is deﬁned as:

(x,y)(k) = [F

(x,y)(k) F

(x,y)(k)]

(2)

LOCALIZATION IN AN AUTONOMOUS UNDERWATER MULTI-ROBOT SYSTEM DESIGNED FOR COASTAL

AREA MONITORING

235

Figure 7: The units are located at layers that have different

ﬂow vectors.

Position of a ﬂoat at position(x,y) at k time step of

EKF:

(x,y)(k) = [x

(k) y

(k)]

(3)

In this project, we set EKF update rate to ∆t =

5minutes. Considering the real data collected by

Finnish Marine Research Institute, we can assume the

current vector is changing slowly both with respect to

horizontal location and time. The current dynamics is

modeled as:

(k) = F

(k− 1) + v(k) (4)

where v(k) is the system error of the current.

Let’s deﬁne the extended state Vector of EKF at

time step k:

X(k) = [P

(k) P

(k) ... P

(k) F

(k)]

(5)

The corresponding extended state equation:

X(k) = A(k)

X(k− 1)



I ∆T

0 I



P(k− 1)

(k)



(6)

Here, system matrix A is updated each time ac-

cording to the depth layers (DL) of respective ﬂoats.

In other words, the prior prediction of the ﬂoat loca-

tion is updated only by the current vector in its present

layer.

The example ∆T Matrix for 3 ﬂoats, while ﬂoat 1

in depth 3, ﬂoat 2 in depth 1, ﬂoat 3 in depth 2:

∆T =





0 0 ∆t

∆ t 0

0 ∆t 0





(7)

All the ﬂoats maintain a depth list of all the known

ﬂoats. Since the measurements that these ﬂoats get

are different, even having the same equations running

internally, they have different estimations of the state

vector. Since the ﬂoats spends most of its time in the

waiting layer, when there is no depth update from the

others, they are assumed to be in the waiting layer

(check Figure 7).

Measurement equation:

Z(k) = [P

GPS1

(k) P

GPS2

(k) ... P

GPSn

(k)

(k) d

(k) ... d

(n−1)n

(k)]

(8)

Expected measurement:

X(k − 1)) = [P

(k) P

(k) ... P

(k)

(k) − P

(k)k kP

(k) − P

(k)k

... kP

(k) − P

n−1

(k)k ]

(9)

To linearize h with respect to X for each update

step:

H(k) = ∂h(

(k− 1))/∂

(k− 1)

(10)

The overall running EKF for k time step:

X(k) =

−

(k) + K

(Z(k) − h(

−

(k))) (11)

where the Kalman gain k is calculated in a stan-

dard EKF formula.

Note that in reality, it is rarely the case that the

ﬂoat will get all the distances from all the other ﬂoats

and the distances between them. And it is only occa-

sionally when the ﬂoat has a GPS ﬁx on the surface

and gets GPS information from the others. In such

case the EKF automatically assumes those measure-

ments are missing and therefore not take them into

account while updating the state variables.

5 TESTS AND RESULTS

The simulated environment contains the ﬂow vectors

and other environmental data that affect the positions

of the ﬂoats. The actual positions of the units are

maintained by the simulator, but they may be mea-

sured by the ﬂoats only in a way that is similar to the

real world, i.e. only the units that are on the surface

know their positions, and distances between ﬂoats are

only measured in seconds of sound travel time. The

user can control whether the units know only the dis-

tances from the unit itself to other units, or if they

know all the distances between all pairs of units. Ev-

ery unit contains a Kalman Filter that estimates the

positions of the units. The estimation is based on

the model that utilizes both the measured distances

between the units and the measured positions of the

units while on the surface. The real and the estimated

positions can be visually tracked and compared in real

time. The following ﬁgures are the comparison result

for a random chosen ﬂoat in the system during their

respective missions.

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5.1 Turning vs. Line Traveling

In an area where the current vectors change quickly,

a ﬂoat will typically make sharp turns. In such cir-

cumstances, its position estimates can be quite inac-

curate. On the other hand, in a case where the currents

are more homogenous, the ﬂoat will follow a approx-

imately straight line and its estimation is obviously

better as time passes.

5.2 Test Conﬁguration

The simulation test area is selected to be a bay area

not far from the Finnish coast. The key parameters

are set as shown in Table 1.

Table 1: Key parameters setting for Simulation.

Acoustic comm. interval once/hour

Surfacing interval 4 times/day

Surfacing time 10 minutes/dive

Comm. level waiting time 5 hours/dive

Meas. depth waiting time 40 minutes/dive

Kalman ﬁlter update interval once/5 minutes

These parameters are consist with the future real

test parameters.

5.3 Baseline Estimation

As a baseline for estimating the quality of the local-

ization method, we use an alternative position esti-

mate which makes no use of acoustic ranging or com-

munication, and instead assumes the current position

to match the latest GPS position ﬁx. See Figure 8 for

demonstration.

0 500 1000 1500 2000 2500 3000 3500 4000

1000

2000

3000

4000

Time (5mins)

Estimation Error (m)

5 Floats Scenario

Baseline Estimate

Figure 8: Estimation error for 5 ﬂoats randomly deployed

within 50m radius area with the the same diving proﬁle.

Error is in Euclidian distance between the estimated and real

position.

5.4 Data Information Exchange

High bandwidth acoustic data exchange between

ﬂoats in the Baltic Sea is extremely difﬁcult. It is es-

pecially true when the ﬂoats are not always staying in

the communication layer, which does not even exist

in some cases. In Figure 9, we show the results from

both only acoustic communication enabled scenario

and acoustic & data exchange enabled case. Both of

them follow the communication probabilitymodel ex-

plained in Section 4.1.

0 1000 2000 3000 4000

500

1000

1500

2000

Time (5 Min)

Estimation Error (m)

Pingpong Only

Data Exchange Enabled

Figure 9: Estimation error comparison between acoustic

ranging only and data exchange scenario. (5 units that de-

ployed in a 50m radius area with same diving proﬁle).

5.5 Deployment Strategy

To deployment pattern of the ﬂoat is an important fac-

tor that has a considerable effect on the outcome of

the whole mission. When the drifters are deployed

within a small perimeter (radius 50m), for example

from an anchored boat, the high probability of ex-

changing information will increase the possibility to

have accurate position estimates. It is especially im-

portant for the drifters at their initialization phase.

Since they will experience almost the same current

vectors throughout their journey, which makes them

most likely remain close to each other throughout the

mission. The downside is of course the fact that the

system can only obtain data from a small proportion

of the area.

When deployed in a large area (radius 2km), the

ﬂoats disperse nicely during the mission. However,

the probability for the ﬂoats to get extra informa-

tion(information besides its own GPS ﬁx when sur-

facing) is very low. It is so low that their estimation of

respective location is of a similar level of the bench-

mark estimation which only rely on GPS ﬁxes.

To comprise, one possible solution is to deploy

ﬂoats into the water in a relatively small perime-

ter, and make their waiting depths slightly different.

Since the current ﬁeld in the Baltic sea is clearly lay-

ered, the current vectors they experience is different.

By the end of the mission the ﬂoats drift nicely away

from each others (up to 6km). Of course, one down-

side for this approach is that, in reality, the communi-

cation channel is correlated to a speciﬁc depth level,

which will lower the probability of data exchange.

See Figure 10.

LOCALIZATION IN AN AUTONOMOUS UNDERWATER MULTI-ROBOT SYSTEM DESIGNED FOR COASTAL

AREA MONITORING

237

0 500 1000 1500 2000 2500 3000 3500 4000

500

1000

1500

2000

Time (5 Min)

Estimation Error (m)

Same Diving Profile

1 Meter Depth Difference Diving Profile

Figure 10: Estimation error comparison between 0m wait-

ing depth (same proﬁle) difference against 1m depth differ-

ence deployment for 5 ﬂoats.

5.6 Current Estimation

One of the main target of the mission is to accurately

measure the subsurface currents speed and directions.

Usually this is of particular difﬁculty since the com-

plex situation underwater is hard to be measured di-

rectly. With the help of acoustic inter-robot commu-

nication, we have achieved some promising results.

Figure 11 and 12 show the current magnitude and

angle estimation generated by Kalman ﬁlter from the

one randomly chosen ﬂoat.

0 500 1000 1500 2000 2500

0.05

0.1

0.15

0.2

Update Number (5 Min)

Current Speed (m/s)

Estimated Current Speed

Real Current Speed

Figure 11: Current estimation magnitude error for 5 ﬂoats

randomly deployed within 50m radius with the same diving

proﬁle.

0 500 1000 1500 2000 2500

100

150

200

Update Number (5 Min)

Current Angle (degrees)

Estimated Current Angle to East

Real Current Speed to East

Figure 12: Current estimation angular error for 5 ﬂoats ran-

domly deployed within 50m radius with the same diving

proﬁle.

There is a small lagging of the ﬁlter result towards

the real environmental current data, which can be ex-

plained by the ﬁlter properties. However, the pre-

liminary results are promising and deserve further re-

search.

5.7 Float Numbers

Obviously, one of the most important factor is the

number of ﬂoats in a given mission. During a mission,

with a greater number of ﬂoats, there are more GPS

ﬁxes and distance measurements to share and there is

a higher probability to gather such information from

the communication between the ﬂoats. See Figures

from 13, 14, 15.

0 1000 2000 3000 4000

1000

2000

3000

Time (5 Min)

Estimation Error (m)

3 floats

15 floats

Figure 13: Estimation error comparison between 3 units and

15 units mission. Deployed in 50m radius area with 1 meter

depth difference in communication level.

0 500 1000 1500 2000

0.05

0.1

0.15

0.2

Update Number (5 Min)

Current Speed

Estimation Error (m/s)

3 floats scenario

15 floats scenario

Figure 14: Comparison of absolute current speed estimation

error between 3 ﬂoats and 15 ﬂoats scenarios. Both scenar-

ios deploy within 50 meter radius and 1 meter waiting depth

difference.

0 500 1000 1500 2000

100

150

200

Update Number (5 Min)

Estimation Error (degree)

3 floats scenario

15 floats scenario

Figure 15: Comparison of absolute current direction esti-

mation error between 3 ﬂoats and 15 ﬂoats scenarios. Both

scenarios deploy within 50 meter radius and 1 meter waiting

depth difference.

The number of ﬂoats in a given mission gives an

interesting topic to research. The performance im-

provement also added a degree of difﬁculty and com-

plexity for acoustic communication, data exchange

via acoustic modem and the localization algorithm.

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238

6 CONCLUSIONS AND FUTURE

WORK

Multi-robot systems have a great inherent potential in

various applications requiring localization and navi-

gation capabilities. In this paper, we have shortly

introduced a multi-robot system suitable for opera-

tions in the shallow water conditions near the coast-

line. The main contribution of this paper is the valida-

tion of our implementation of a localization algorithm

in these demanding conditions. The obtained results,

which are drawn from extensive simulations based

on real case scenarios and environmental data, have

proved the validity and effectiveness of our approach

for the localization of these intelligent autonomous

proﬁling ﬂoats.

However, there is much more yet to be explored in

terms of our research. We will brieﬂy introduce a bet-

ter underwater communication channel modeling, in

particular for the layered Baltic sea case. Further tests

scenarios with different surfacing intervals, acoustic

communication intervals and dynamic mission plan-

ning will be conducted. Later research will also

include optimization between communication band-

width, energy and reliability, data compression in data

communication and other ﬁltering algorithms. Real

world robotic tests with renewed four unit system will

be started in summer 2010 and they will further guide

the future research.

ACKNOWLEDGEMENTS

We would like to thank the Academy of Finland for

funding the Finnish Center of Excellence in Generic

Intelligent Machines (GIM), the SWARM consor-

tium, Jorma Selk¨ainaho, Janne Paanaj¨arvi , Sami

Terho from GIM, and Antti Westerlund (Finnish Me-

teorological Institute).

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