Fuzzy-DL Perception for Multi-Robot Systems

Markus Eich, Ronny Hartanto

DFKIRobotics Innovation Center

Bremen, Germanymarkus.eich@dfki.de

ronny.hartanto@dfki.de

Abstract

For future planetary robot missions, multi-robot-systems can be considered as a suitable platform toperform space mission faster and more reliable. Inheterogeneous robot teams, each robot can have dif-ferent abilities and sensor equipment. In this paperwe describe a lunar demonstration scenario where ateam of mobile robots explores an unknown area andidentifies a set of objects belonging to a lunar infras-tructure. Our robot team consists of two exploringscout robots and a mobile manipulator. The missiongoal is to locate the objects within a certain area, toidentify the objects, and to transport the objects toa base station. The robots have a different sensorsetup and different capabilities. In order to classifyparts of the lunar infrastructure, the robots have toshare the knowledge about the objects. Based onthe different sensing capabilities, several informationmodalities have to be shared and combined by therobots. In this work we propose an approach usingspatial features and a fuzzy logic based reasoning fordistributed object classification.

1 Introduction

In this paper we present our latest results of theproject IMPERA (Integrated mission planning usingheterogeneous robots). The main goal of the projectis the development of a planning and a plan execu-tion architecture using a team of robots within a lu-nar scenario. For future lunar and other planetarymissions, system autonomy becomes more and moremandatory. Current NASA missions (e.g. Mars Ex-ploration Rover1 and Mars Science Lab2) are dealingmainly with the exploration of the Mars surface andthe analysis of the surface. The systems Spirit, Op-portunity, and Curiosity work thereby as individualsystems. Looking into the future, it is a likely scenario

1www.nasa.gov/mission pages/mer/index.html2www.nasa.gov/mission pages/msl/index.html

to install infrastructure and other scientific compo-nents on Mars or on the Moon. This infrastructurecan consist of small stations measuring environmentalconditions, units used for providing drill cores for sub-surface analysis, or modules for communication andenergy supply. In order to perform a mission coopera-tively, the robots have to share a common knowledgeabout the environment, such as the type of objectsand modules the robots have to identify, or the loca-tion of these objects.

Figure 1. : Left: The mobile manipulator robotAMPARO. Right: The scout robot used for visualdetection of infrastructure modules.

In our scenario, the robot team consists of threerobots having different sensing abilities. The robotAMPARO (cf. Figure 1, left) uses a tilting 3D laserrange finder to generate 3D point clouds of the envi-ronment. These data are used to detect three dimen-sional objects during a fetch and transport phase ofthe mission. Two scout robots, based on the PioneerAT robot (cf. Figure 1, right), are used for coop-erative map building and object candidate detectionusing monocular color images during the explorationphase of mission. The objects used during our demon-stration scenario are shown in Figure 2. The missionconsists of the sequentially executed tasks of explor-

Figure 2. : The three objects which need to be de-tected by the robot team during the mission: batterypack (yellow), soil sample containers (blue), base sta-tion (red).

ing an unknown environment while simultaneously lo-cating three distinct objects. The objects consists ofa battery pack, a soil sample container and a basestation to which the other two objects have to betransported. The described scenario is an extensionto the scenario described in [3] where only cylindricalsample containers are regarded. In this work, we ex-tended the concept to other regular shaped objects,such as battery packs and the base station. In ourapproach we use spatio-semantic knowledge aboutthe environment in order to classify the objects [2].Spatio-semantic description is based on the conceptsthat objects can be described how they “look like” interms of shape features (e.g. planar, cubical, cylin-drical), spatial features (e.g. size, extension, orien-tation), spatial relations, and color. Spatio-semanticdefinitions can be extracted directly from the sen-sor data (e.g. using point cloud processing in com-bination with segmentation and cluster analysis) andare describable in a spatio-semantic ontology, such as“The soil sample container is a blue cylinder and isperpendicular to the ground” or “The battery pack isa yellow box and the battery pack has an edge lengthof 20 cm”.

Extracted spatial features cannot be matchedagainst a discrete ontology (due to sensor noise, oc-clusions, etc.), therefore an imprecise (fuzzy) knowl-edge base has to be modeled to estimate the bestmodel match. A constraint network is one way forobject classification [2]. Most of the basic perceptionapproaches extract and analyze clusters within thepoint cloud prior to object classification. Some 2Dand 3D shape extraction algorithms are given in [11]for 2D shapes and in [10] for 3D shapes. Semanticperception methods, which also take semantics intoaccount, are presented in [5]. The authors describein their work the bridging between the spatial domain

and the semantic domain which they call S-Box (spa-tial box) and T-Box (taxonomy box). The semanticinterpretation of physical objects is done by opticalmarker identification but not directly on spatial inter-pretation of point cloud data. In approach describedin [7], a constraint network is used to identify spatialentities such as walls, floors or doors.

2 System Overview

The system architecture presented in this workconsists of several independent modules which arerunning on distinct robots. On each robot we usethe Robot Operating System (ROS) as a communi-cation backbone between the modules [9]. The over-all architecture and its components are depicted inFigure 3. In order to establish the communication

DataADist ribut ionAService(DDS)

Scout A1

ROSAtoADDSAProxy

Scout A2

3DAFuzzyPercept ion

Module

Act ionAStateAMachine

LocalASLAMModule

Explorat ionCoordinat ion

Module

Am paro

Manipulat ionModule

2DAPercept ionModule

Act ionAStateMachine

LocalASLAMModule

Explorat ionCoordinat ion

Module

Figure 3. : System architecture of the robot compo-nents.

between the systems, we make use of a reliable, cloudbased communication interface based on a commer-cial version of the Data Distribution Service (DDS)[8]. DDS is based on the loosely coupled publish sub-scribe paradigm and provides several QoS options likeautomatic re-connection and data buffering in caseof communication loss. More details about the inter-robot communication framework is given in Section 3.The DDS cloud ensures that each robot has the sameknowledge about the internal status of each individ-ual robot. Some information, which is distributedvia DDS has to be stored persistent in order to en-able the robots to perform actions, based on the com-mon knowledge base about the mission and the en-vironment. This information is taken directly fromthe DDS cloud and stored as a local copy on each

individual robot. The DDS cloud itself stores onlyinformation needed by other robots for coordination,especially during the exploration and mapping phase.Some information has to be stored persistently, suchas the poses and the IDs of detected sample contain-ers.

3 Cloud-Based Multi-RobotCommunication

All participating robots in the team have to ex-change data constantly in order to achieving a com-mon goal. One challenge in the multi-robot commu-nication is the limited resource and the availabilityof the network. In a large area, a robot can lose theconnection with the other robots due to the distance.However, all messages from one robot to the othersystems have to be maintained and have to be guar-anteed to be delivered. Having a limited resourceor bandwidth, it is not possible to have a communi-cation protocol which allows request of certain mes-sages, e.g., based on the sequence IDs or time in-terval. This kind of communication can introducehuge network traffic, depending on the number of in-volved robots. In [3, 6], a transparent communicationnetwork based on publish/subscribe paradigm usingData Distribution Service [8] (DDS) is described indetail. The advantages of this approach are trans-parency and robustness. All messages will be deliv-ered successfully from the publisher to the subscriberswithout any explicit commands. Thus, this approachis resource efficient as each message will be deliveredonly once from the source to the destinations. Themessages are guaranteed to be delivered to all sub-scribers, even on a temporarily network outage.

Another important aspect of the cloud-basedcommunication is the modeling of the messages for-mat. A well defined message format can mini-mize the required bandwidth for communicating themamong the agents and reduce further post-processingtasks. Two message formats are used for sharing ob-jects information between robots. The first message,“candidate-object”, is used by the scouts for publish-ing the detected object candidates with respect tothe map. The second message, “identified-object”, isused by the AMPARO for publishing the identifiedobject with their properties.

The scout robots detect the object candidates us-ing a monocular color camera, thus the position of theobject cannot be extracted from the camera data dueto the missing depth information. However, the posi-tion while detecting the candidate can be used by theAMPARO later on for the identification of the ob-jects. Depending on the size and position of the color

blob on the camera data, a probability function canbe calculated based by other agents from the sharedinformation. The candidate-object (CO) message ismodeled as a 5-tuple as follows:

CO = (RID, Oprop, PRID, ORID

, C)

where: RID is the ID of the robot who detectsthe object candidate, e.g. scout1. Oprop is is thecolor property of the object, e.g. yellow, blue, orred. PRID

and ORIDare the position in cartesian

coordinate (px, py, pz) and orientation in quaternion(ox, oy, oz, ow) of the robot while detecting the ob-ject candidate. C is the two-dimensional covariance(cx, cy) defining the certainty of the object candidate,depending on the size and position of the color blobon the camera data.

Once the candidate object message is publishedto the cloud, all participating robots will receive theshared information. The candidate object messageenables the agents to calculate the two-dimensionalprobability density function on their map. The func-tion is defined as follows:

f(map, µ,Σ) =1√

|Σ|(2π)2e−

12 (map−µ)Σ−1(map−µ)′

Σ = R ∗[cx 00 cy

]∗R′

µ =

[pxpy

]where: map is two-dimensional map of the environ-ment and R is the rotation matrix extracted from theyaw angle of the ORID

.

AMPARO can use the probability density func-tion (PDF) from the candidate object message forcalculating a pose where it can start identifying theobject. Sometimes, an object can be found by differ-ent robots, thus multiple candidate object messagesreferring to the same object are broadcasted throughthe cloud. Processing these PDFs could infer the po-sition of the object itself instead of the location wherethe scouts detected it. As a result, AMPARO couldalso compute the ideal pose for identifying the objectfrom the inferred object pose.

Once the object is successfully identified, the in-formation about the identified object is shared to allparticipating robots through the cloud. The identi-fied object message is modeled as such for enablingfurther tasks by other agent, e.g. object manipula-tion. The identified object (IO) message is definedas 8-tuple, as follows:

ID = (OID, Otype, Ocolor, Obbox, Olikelihood,

POID, OOID

, COID)

where: OID is the ID of the identified object thatuniquely generated for the overall mission. Otype isthe object type from the enumerated value, e.g. bat-tery, soil-sampled, etc. Ocolor is the color of the iden-tified object. Obbox is the dimension of the identifiedobject represented in bounding box, which later onbe used for calculating the grasping pose. Olikelihoodis the likelihood of the perception module that tellinghow good the object is being identified as a typeOtype. POID

and OOIDare the position and orien-

tation of the identified object in cartesian coordinateand quaternion. COID

is the covariance of the iden-tified object on the map.

4 Semantic Perception

4.1 Spatial Feature Description andExtraction

In our demonstration scenario, the team of robotshas to classify the objects shown in Figure 2. Theobjects are first roughly located as candidates usingthe faster scout robots, using only monocular sens-ing capabilities. The robots can detect the approx-imate position within the map but no informationabout the accurate 3D pose of the objects. Oncethe candidates are identified, the robot AMPAROmoves to the estimated location of the objects anduses the tilting 3D laser range finder for object lo-calization and classification. For the detection pro-cess, we introduce a perception pipeline, depicted inFigure 4. To extract the spatial features from the

Point Cloud Generation

Clusterization

Model Checking

Plane Extraction

Generation of Fuzzy Scores

Generation of Fuzzy Relationships

Plane Feature Extraction

Spatial Feature Extraction

Geometric Level

Semantic Level

Figure 4. : Perception Pipeline

3D point cloud, we use a RanSaC-based segmenta-tion approach described in [10]. The estimation ofthe spatial relationship between the objects and theground plane is fundamental in our approach, there-fore the ground plane is segmented and classified first.

This is achieved using a region growing algorithm, de-scribed in [2]. The remaining points, not belongingto the ground plane, are clusterized. In the follow-ing step, the spatial features are extracted from theseremaining clusters. The objects (cf. Figure 2) are ba-sically cylindrical shapes (the soil sample container)and cuboids (base station and battery pack). Thebase station is a cuboid which has a vertical slot onone side and a switch on the other opposide side. Thedetection of the soil sample containers using spatialfeatures is described in detail in [3]. The spatial fea-tures we use for the detection are summarized below:

Φ1 = CylinderModelF it |Φ1 ∈ R, 0 ≤ Φ1 ≤ 1

Φ2 = Radius |Φ2 ∈ RΦ3 = Height |Φ3 ∈ RΦ4 = Orientation |Φ4 ∈ R3

Φ5 = Position |Φ5 ∈ R3

Φ6 = HeighTo |Φ6 ∈ RΦ7 = OrthoTo |Φ7 ∈ R, 0 ≤ Φ7 ≤ 1

Φ8 = ParallelTo |Φ8 ∈ R, 0 ≤ Φ8 ≤ 1

For the cuboids used in our scenario (i.e. the base sta-tion and the battery pack), additional features haveto be extracted from the 3D point clusters. Insteadof searching directly for cuboids in the point cloud, amore general approach has been selected, based on adivide and conquer strategy. In this approach, onlyplanar (2D based) planes have to be extracted. Theplanar features used are have been selected based onthe following observation of the objects. These obser-vations can be described using an informal descriptionlogic representation: a) The base station is a cuboidand the base station has a vertical, rectangular sloton the rear side. b) The battery is a cuboid. c) Thebox has three planes which are orthogonal to eachother. d) In a cuboid model, three orthogonal planesshare a common edge. e) The base station has planarcomponents named “panels”. f) All side panels arerectangular.

Given the observations above, the list of featuresΦ1 − Φ8 is extended in order to check the extractedfeatures with a reasoner.

Φ9 = PlanarModelF it |Φ9 ∈ R, 0 ≤ Φ9 ≤ 1

Φ10 = Rectangular |Φ10 ∈ R, 0 ≤ Φ10 ≤ 1

Φ11 = HasCommonEdge |Φ11 ∈ R, 0 ≤ Φ11 ≤ 1

Φ12 = Area |Φ12 ∈ RΦ13 = maxExtension |Φ13 ∈ R

The features described above are directly extractablefrom the planar clusters of the point cloud. For de-

tails, see [2, 4]. Note that the features Φ9−Φ11 are im-precise definitions and not binary true/false assump-tions in a logical sense. The idea behind this is thatby using fuzzy sets in a logic expression, sensor noise,measurement errors, and occlusions can be covered.In other words, the specific features are defined bya membership function calculating the likelihood ofhow extracted features match the features given ina knowledge base. With the definition of the spatialfeatures, a spatio-semantic ontology can be definedwhich is described in the next section.

4.2 Spatio-Semantic Object Ontology

In this section, we introduce our method on howperceivable features of objects and the environmentcan be matched with a knowledge based system. Se-mantic object annotation is in this case accomplishedby ontology queries. In our multi robot scenario, onlythe robot AMPARO has currently the ability to per-ceive the environment using a 3D point cloud. Duringthe mission the scout robots estimate the positions ofthe objects based on color segmentation and basedon the robots position as described already in Sec-tion 3. The robot AMPARO moves to the vicinity ofthe estimated object poses and initiated the percep-tion pipeline.

The knowledge about the relevant objects and thespatial relation are defined in the T-Box (describingthe terminology of the ontology). The A-Box de-scribes the assertion part of the ontology, i.e. theindividuals. The ontology of the domain is manuallygenerated using the spatial knowledge about the sam-ple containers in scope. The geometric features aredescribed as concepts of the T-Box. The individualsof the knowledge base are automatically generated bythe perception layer (cf. Figure 5). The A-Box is up-dated after each 3D scan and the FuzzyDL reasoneris triggered to classify the soil sample container, thebase station and the battery pack within the pointcloud, based on the spatio-semantic description of theT-Box. In this paper, we extend the fuzzy descriptionlogic based approach described in [3]. The key idea isto describe the knowledge base for object classifica-tion using an ontology which can deal with vaguenessand membership values. FuzzyDL as a knowledgebase and the corresponding reasoner was firstly in-troduced in [1]. The language defined by FuzzyDL isgiven by

C,D := >|⊥|A|C tD|C uD|¬C|∀R.C|∃R.C

where C and D are concepts, A defines the atomicconcept, and R.C the role of a concept. For our spa-tial reasoning approach we make also use of the Godelt-norm and Godel t-conorm to express FuzzyDL

Ontology

Spatial Description

Feature Exraction

A-BoxT-Box

Reasoning

Fuzzy DL

Perception

Figure 5. : The reasoning architecture using De-scription Logic. The A-Box is provided by the fea-ture extractor from the perception side. The T-Boxis modeled using FuzzyDL syntax.

union and intersection (i.e. C uG D := min(C,D)and C tG D := max(C,D) respectively. For a de-tailed description of the FuzzyDL semantic, pleaserefer to [1]. In order to model the ontology of theobjects, the following concrete names are assigned tothe different DL concepts and roles (concepts startwith capital letters while roles and functionals startwith a small letters):

Listing 1: The T-Box of the Knowledge Base

GroundPlane l i n e a r ( 0 , 1 , 0 . 7 , 0 . 5 ) )CylinderType l i n e a r ( 0 , 1 , 0 . 7 , 0 . 5 ) )Plane l i n e a r ( 0 , 1 , 0 . 7 , 0 . 5 ) )Rectangular l i n e a r ( 0 , 1 , 0 . 7 , 0 . 5 ) )S l o t l i n e a r ( 0 , 1 , 0 . 7 , 0 . 5 ) )Red l i n e a r ( 0 , 1 , 0 . 5 , 0 . 5 ) )Blue l i n e a r ( 0 , 1 , 0 . 5 , 0 . 5 ) )Yellow l i n e a r ( 0 , 1 , 0 . 5 , 0 . 5 ) )

SampleContainer ( and( some isCyl inderType CylinderType )( some i sOrthogona l GroundPlane )(>= hasHeight 0 . 1 )(<= hasHeight 0 . 2 )(<= hasRadius 0 . 1 ) ( some i sB lue Blue ) )

FrontPanel ( and( some i sP lane Plane )( some i sRec tangu la r Rectangular )( not S l o t )(>= hasShortEdgeLength 0 . 15 )(<= hasShortEdgeLength 0 . 25 )(>= hasLongEdgeLength 0 . 25 )(<= hasLongEdgeLength 0 . 35 ) )

RearPanel ( and( some i sP lane Plane )( some i sRec tangu la r Rectangular )( some hasS lo t S l o t )(>= hasShortEdgeLength 0 . 15 )(<= hasShortEdgeLength 0 . 25 )(>= hasLongEdgeLength 0 . 25 )(<= hasLongEdgeLength 0 . 35 ) )

TopPanel ( or( and( some i sP lane Plane )( some i sRec tangu la r Rectangular )( not S l o t )(>= hasShortEdgeLength 0 . 15 )(<= hasShortEdgeLength 0 . 25 )(>= hasLongEdgeLength 0 . 35 )(<= hasLongEdgeLength 0 . 45 ) )( some i s P a r a l l e l GroundPlane ) )

SidePanel ( and( some i sP lane Plane )( some i sRec tangu la r Rectangular )( not S l o t )(>= hasShortEdgeLength 0 . 25 )(<= hasShortEdgeLength 0 . 35 )(>= hasLongEdgeLength 0 . 35 )(<= hasLongEdgeLength 0 . 45 )( some i sOrthogona l TopPlane ) )

BaseStat ion ( and( or( some hasFrontPanel FrontPanel )( some hasRearPanel RearPanel )( some hasSidePanel SidePanel )( some hasTopPanel TopPanel ) )( some isRed Red) )

BatteryPack ( and( some isSmallBoxType SmallBoxType )( not S l o t )(<= hasLongEdgeLength 0 . 2 )( some i sYe l l ow Yellow ) )

The ontology depicted in Listing 1 shows the knowl-edge base of the objects in our scenario. The fea-tures described in the T-Box of the knowledge baseare matched with the object candidates. The colorof the candidates is identified by the scouts using themonocular camera. The other spatial features areextracted from the 3D point cloud, provided by AM-PARO. For instance, the knowledge base for the basestation is interpreted as follows: The base station hasat least one visible panel and the panel is red. Thepanels may be one of the type “front panel”, “rearpanel”, “top panel”, “side panel”. The side panel isan entity which has no slot, is planar, rectangular,and the longest edge of the panel is between 35 cm

and 45 cm (the actual model has exactly 40 cm). Theoutput of the reasoner in this case is the likelihoodof a perceived object belonging to a defined objectconcept. Fuzzy concepts are described using spatio-semantic features (cf. Figure 6). In the T-Box we use

1

a b c a b

11

a b c d

a

1

b 1

a) b) c) d)

Figure 6. : The used fuzzy concepts for the spatialreasoning approach. The features are represented us-ing the fuzzy concepts of triangular set (a), crisp set(b), right shoulder (c) and the linear modifier (d).

for our experiments(cf. Listing 1), only linear con-cepts are used. To query the knowledge base, afterthe individuals are updated in the A-Box of the on-tology, a min-instance query is executed in order tocheck the satisfiability of the models for The threeobjects base station, sample container and batterypack. This is achieved by inf{n|K |= (instance objectiBaseStation n)}, inf{n|K |= (instance objecti Sam-pleContainer n)}, and inf{n|K |= (instance objectiBatteryPack n)} respectively.

5 Experiments and Results

Figure 7 shows the map with the calculated PDFsfrom the candidate object messages on the cloud. Ad-ditionally, the positions of the objects are overlaidon the map for the illustration purpose. Due to thecoordinated exploration, the exploration tasks weredistributed among the scout robots by minimizingoverlapping areas. Due to the start location and po-sition of the object, both scouts were detecting thered object from different viewpoints. The figure alsoshows that scout1 had higher probability, smaller buthigher PDF, than scout2. This is due to the size andposition of the color blob on the camera. Table 1shows some properties of the candidate object mes-sages. This information was used for calculating thePDFs in Figure 7. The second part of the exper-

Table 1. : Object candidate messages

Robot ID Color Pos (x,y) Covar. (x,y)scout1 red (3.84, 4.65) (0.1, 0.5)scout1 blue (18.17, 1.68) (0.3, 1.25)scout2 yellow (-2.83, 7.30) (0.25, 1)scout2 red (3.30, 6.52) (0.25, 2.5)

iment is dealing with the perception of the defined

x

y

scout2 pos1

scout2 pos2

scout1 pos1 scout1 pos2

Figure 7. : Experiment map with object candidates’ messages.

(a) (b) (c) (d)

(e) (f) (g) (h)

Figure 8. : Experimental setup for the 3D FuzzyDL perception method described in this paper. The objectswere arranged in different locations, distanced and orientations with respect to the perceiving AMPARO robot.The corresponding detection results are given in e)-h). The detected ground plane is visualized in green andthe objects in their corresponding color including the detected bounding box.

objects using the FuzzyDL-based semantic perceptionapproach described in this paper. Figure 8 show theexperimental setup. To verify the approach, several3D point cloud scans have been recorded and the ob-ject features have been extracted using the approachdescribed in Section 4. The A-Box of the ontologyhas been filled with the extracted spatial entities, con-sisting of planar clusters, cylindrical clusters, and alsoundefined shapes which do not match any object. Ta-ble 2 gives the perception and the FuzzyDL reasoningresults. Table 2 gives the number of potential enti-ties extracted from the 3D point cloud. The over-all likelihoods are given after the reasoning is com-pleted for the objects “base station” (BS), “samplecontainer” (SC) and “battery” (BAT). For the base

station, the number of detected components is given(e.g. slot, number of matched panels). The BS andBAT components describe how many spatial entitiesdo match with the object components (i.e. sides orslot). The maximum likelihood is given after the rea-soner matched the shape and spatial relationship withthe object ontology. Therefore the number of poten-tial components can be large, before the reasoner fil-ters mismatched features.

6 Conclusions

In this paper we described our approach of us-ing a multi-robot system to identify objects withina lunar environment. As an example, three objects

Table 2. : Perception results using spatial feature extraction and spatial reasoning based on Fuzzy DescriptionLogic. The scenes are corresponding to Figure 8

Scene Entities BS components BS score BAT components BAT score SC score(a) 3 2 0.77 1 0.76 0.92(b) 21 7 0.94 25 0.97 0.0(c) 24 5 0.0 55 0.0 0.83(d) 25 3 0.0 12 0.84 0.0

were provided. The spatial features of the objects aregiven in a Fuzzy Description logic based ontology. Ateam of mobile robots explores the area and identifiesthe objects based on different sensor modalities. Theinteraction between the systems and the exchange ofthe knowledge base about the objects is provided bya reliable DDS (Data Distribution Service) interfacewhich implicitly prevents data loss. The semanticclassification of three objects has be verified duringexperiments. In a next step we will extend the seman-tic perception approach to more basic shapes as wellas free-form shapes. The reasoner currently relies onthe fact, that the objects are compound objects con-sisting of planes, rectangular shapes and cylindricalobjects. An upcoming research topic is the classi-fication of free form shapes, such as NURBS (Non-Uniform Rational B-Splines) or free form shapes con-sisting of basic shapes and NURBS.

Acknowledgment

This work is partially supported by the IMPERA

project and the INCASS project (MOVE-FP7-605200-

INCASS). The project IMPERA is funded by the German

Space Agency (DLR, Grant number: 50RA1111) with

federal funds of the Federal Ministry of Economics and

Technology (BMWi) in accordance with the parliamen-

tary resolution of the German Parliament.

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