robox at expo.02: a large-scale installation of personal...

Robotics and Autonomous Systems 42 (2003) 203–222

Robox at Expo.02: A large-scale installation of personal robots

Roland Siegwart∗, Kai O. Arras, Samir Bouabdallah, Daniel Burnier,Gilles Froidevaux, Xavier Greppin, Björn Jensen, Antoine Lorotte,Laetitia Mayor, Mathieu Meisser, Roland Philippsen, Ralph Piguet,

Guy Ramel, Gregoire Terrien, Nicola TomatisAutonomous Systems Lab, Swiss Federal Institute of Technology Lausanne (EPFL), CH-1015 Lausanne, Switzerland

Abstract

In this paper we present Robox, a mobile robot designed for operation in a mass exhibition and the experience we madewith its installation at the Swiss National Exhibition Expo.02. Robox is a fully autonomous mobile platform with uniquemulti-modal interaction capabilities, a novel approach to global localization using multiple Gaussian hypotheses, and apowerful obstacle avoidance. Eleven Robox ran for 12 hours daily from May 15 to October 20, 2002, traveling more than3315 km and interacting with 686,000 visitors.© 2003 Elsevier Science B.V. All rights reserved.

Keywords:Personal and service robot; Global localization; Obstacle avoidance; Human–robot interaction; Long-term experience; Tour-guiderobot

1. Introduction

In this paper we adopt an experimental view of theproblem. After the problem specification of mass ex-hibitions, we briefly present issues regarding mechan-ical design, software architecture and safety. Focusis then given to the multi-modal interaction systemSOUL and the enhanced navigation system able tohandle the highly dynamic environment of the exhi-bition. The paper concludes by presenting results ofthe robot’s performance and the visitor’s experience.

During its operation of 159 days from May 15 toOctober 20, 2002, the 11 Robox autonomously guidedvisitors through theRobotics Pavilionat theSwiss Na-tional Exhibition Expo.02, up to 12 hours per day,

∗ Corresponding author. Tel.:+41-21-693-38-50;fax: +41-21-693-78-07.E-mail address:[email protected] (R. Siegwart).URL: http://asl.epfl.ch

seven days a week (Fig. 1). Throughout this period,686,000 people were in contact with a personal robotfor the first time in their life. The scale of the instal-lation and the fully autonomous navigation and inter-action of the robots make this project a milestone inmobile robotics research. It served as an extraordinaryresearch platform and enables better understanding ofthe needs and concerns of the public in respect withthe future of personal robot technology.

2. Related work

Progress in the application of estimation and deci-sion theory combined with recent advances in sensorand computer technology enable today reliable nav-igation and interaction in highly dynamic real worldenvironments. A limited number of researchers haveaddressed the challenge of navigation in exhibition-like environments such as museums[7,13,19,24,26].

0921-8890/03/$ – see front matter © 2003 Elsevier Science B.V. All rights reserved.doi:10.1016/S0921-8890(02)00376-7

204 R. Siegwart et al. / Robotics and Autonomous Systems 42 (2003) 203–222

Fig. 1. (a) The autonomous exhibition robot Robox. (b) RoboxNo. 6 with visitors in the pavilion at Expo.02.

Rhino[7] and Minerva[24] were both successfullydeployed in a museum during 1 or 2 weeks, respec-tively. Their task was to give guided tours to the vis-itors. Their localization method relied on raw laserrange and vision data. Using raw data in a highly dy-namic environment makes it hard to decide which sen-sor reading shall be taken for localization and whichone is to be discarded. The filter which was used in[7,24] relied on the heuristic that range readings fromdynamic obstacles are typically shorter than an ex-pected range from the map. Unfortunately, the robotposition must be known in order for this to work. Also,both robots were not autonomous with respect to com-putation, i.e. localization was running on off-boardhardware requiring a working radio link.

Unlike these short-term projects, the autonomoustour-guide robot Sage was in action permanently ina museum with a total of more than half a year ofsuccessful operation[19,26]. For localization, theenvironment was substantially modified by addingartificial landmarks (color patches). The approachperforms well but limits the movements of the robotto a predefined set of unidirectional safe routes,which guarantee constant landmark visibility. Obsta-cle avoidance just stops the robot such that it doesnot deviate from these routes.

A multi-robot installation, which is operationalsince March 2000, is presented in[13]. As a perma-nent part of a museum, three self-contained mobile

robots have the task to welcome visitors, offer themexhibition-related information and entertain them.Their navigation area is restricted and very wellstructured. Localization uses segment features and aheuristic scheme for matching and pose estimation.

Finally, the 72-robot installation at the World FairExpo 2000 in Hannover, Germany, was the first ap-plication of mobile robots in a mass exhibition. Thevehicles, however, were very low-tech. Localized andcontrolled from external infrastructure, they served asfreely moving swarm entities forming a huge interac-tive art installation during the 6 months of Expo 2000(there is no publication to the knowledge of the au-thors).

3. Problem statement

The Swiss National Exhibition takes place aroundevery 40 years. The recent edition, Expo.02, took placefrom May 15 to October 20, 2002. It was a majornational happening with 37 exhibitions and a richevent program. The exhibitionRoboticswas intendedto show the increasing closeness between man androbot technology (Fig. 2). The central visitor expe-rience of Robotics was the interaction with 11 au-tonomous, freely navigating mobile robots (Fig. 1) ona surface of about 315 m2. Their main task was givingguided tours but included also a robot taking picturesof visitors. The exhibition was scheduled for 500 per-sons per hour. For this task, the main specificationscan be summarized as follows:

• Navigation in unmodified, highly populated envi-ronment with visitors and other freely navigatingrobots.

• Bi-directional multi-modal interaction using easy-to-use, intuitive yet robot-typical interaction modal-ities. Speech output in four languages: French,German, Italian and English.

• Safety for visitors and robots at all time.• Reliable operation during up to 12 hours per day, 7

days per week, during 5 months.• Minimal manual intervention and supervision.• Adaptive multi-robot coordination scenarios in

function of the number of visitors and their inter-ests.

• Control of visitor flow by the robots.

R. Siegwart et al. / Robotics and Autonomous Systems 42 (2003) 203–222 205

Fig. 2. Definition of robotics through the proximity and level of interaction between humans and robots: (a) industrial robotics, (b) personalrobots and (c) cyborgs.

• Development of 11 robots within tight budgets andschedules.

After an outline inSection 4on how these pointshave been addressed in the design of the robots, theremainder of this paper focuses on the most importantcompetencies of Robox, which are the multi-modal in-teraction and navigation system. The paper concludeswith results from the operational experience and inter-pretations of the robots’ performance and the visitors’feedback.

4. The mobile robot Robox

The personal robot Robox is the result of aninterdisciplinary project team involving engineers,exhibition makers, and industrial designers. The de-velopment of two prototypes started in January 2001while the dedicated software development started inApril 2002, only 13 months before the exhibitionstart. This short development time combined with avery tight development budget of around $ 400,000defined the challenging pre-conditions of the project.Based on the Autonomous System Lab’s experiencein robot design, system integration and long-termexperimentation[4,5], we concluded that building arobot from scratch[25] was the best way to fulfill therequirements regarding functionality and design.

4.1. Basic functionalities

The basic functionalities of the exhibition robotRobox are shown inFig. 3. Its hardware consists ofthree main components:mobility, interactivity andsafety (Fig. 4). The base of the robot contains the

Fig. 3. Functionalities of the exhibition robot Robox.

Fig. 4. Hardware architecture.


Compact-PCI controller, the two laser range finders(Sick LMS 200), the drive motors, the redundantsafety circuit (seeFig. 4), the batteries, and thebumpers that contain tactile sensors. It is motorizedby two differentially actuated wheels on the middleaxis using 1:50 harmonic drives and a castor on thefront and rear, one of them on a spring suspension.This gives Robox a good maneuverability and stabil-ity in spite of its height of 1.65 m. The battery packgives the robot an autonomy of around 12 hours andrequires 12 hours for recharging.

The upper part of the robot incorporates the inter-action modalities (Fig. 3). The face includes two eyeswith independently actuated pan-tilt units and mechan-ically coupled eyebrows. The right eye is equippedwith a Firewire color camera for people tracking. Theleft eye contains a LED matrix to display symbolsand icons. A high quality speech synthesizer enablesthe robot to speak in the four required languages. Thecentral input device enabling a bi-directional commu-nication with the visitors are four buttons which al-low selecting the language, responding to questionsthe robot asks, and other types of actions. Two of the11 Robox are additionally equipped with a directionalmicrophone matrix for speech recognition. Althoughspeaker-independent speech recognition in four lan-guages in an exhibition-like context is not possible to-day, on-site experiments demonstrated the feasibilityof recognizing simple words likeyesand no in fourlanguages.

4.2. Hardware architecture

The navigation software is considered safety-criticaland is running on the hard real-time operating sys-tem XO/2 [5] installed on a PowerPC 750 (G3) at380 MHz. The PowerPC interfaces the two laserscanners, a camera looking to the ceiling (via aBt848-based frame grabber), the tactile sensors, thesafety circuit and the drive motors/encoders (Fig. 4).It communicates with the interaction PC through theon-board Ethernet.

The interaction software, which is not consid-ered safety-critical, is running on a Pentium IIICompact-PCI board at 700 MHz under Windows2000. This allows using standard software tools witha wide availability of drivers and libraries for vision,speech synthesis and speech recognition. The PC has

access to the Firewire eye camera, the controller foreye pan-tilts and eyebrows, the input buttons, the LEDmatrix, the microphone, and the two loudspeakers.Via a domotic system, the PC can furthermore inter-act with elements in the environment: switch on andoff light spots, trigger flashes, start demonstrations orcontrol other exhibits via infrared.

The robot is linked via radio Ethernet with anoff-board PC that serves as a supervision and loggingtool only. It displays important state variables of therobot at any time and stores relevant log informationon a daily basis. However, Robox does not rely onthis connection because it is always running from itson-board computers in fully autonomous mode.

4.3. Software architecture

As mentioned, the robot controller contains an IntelPentium and a Motorola PowerPC board. The softwarewas first designed on an abstract level. In a second step,functional units were assigned to one of the two com-puters accounting for three different criteria: hardwarerelation, safety and availability. For hardware relatedobjects the choice was usually obvious (e.g. the SickLMS 200 to the PowerPC). Safety and time-criticalobjects were assigned to the PowerPC due to the func-tional and temporal guarantees provided by the XO/2operating system. Objects requiring commercial,off-the-shelf components have been implemented onthe Windows-based machine because of their wideravailability (e.g., speech out, Firewire eye camera).

The resulting software architecture is shown inFig. 5. Watchdogs are implemented for the speedcontroller, the obstacle avoidance, the bumpers, andthe laser scanner driver. Failure of one of these tasksis detected by the security controller, which attemptsto restart the crashed task or, if the restart fails, stopsthe vehicle and sends an e-mail to the operator. Thesecurity controller furthermore takes care of the hard-ware watchdog, which is used by the PIC processor. Amissing watchdog signal within two cycles (200 ms)means that either the security controller or the oper-ating system is not running properly. In this case, thePIC circuit safely stops the whole system. Further-more, this circuit ensures that the pre-defined maxi-mal speed is never exceeded and that the PowerPCresponds to each bumper contact. The central objectSOUL [16] of the interaction subsystem that enables


Fig. 5. Software architecture.

to create and control the scenarios is described inSection 6.

5. Navigation system

A mass exhibition imposes different challenges forthe navigation system:

• Safety aspects are very important.• The environment is cluttered with people and thus

very dynamic.• The visitors of the exhibition have no experience

with mobile robots and thus their behaviors can bevery strange and surprising.

• The robot has to fulfill its tour-guide task even ifpassing through the crowd is very difficult.

Furthermore, the navigation system has to be easilyaccessible by the scenario objects in order to executedifferent tours and interactions, and it has to run inreal time on-board the robot.

In order to fulfill these requirements and specifica-tion, we combined graph-based global planning withfeature-based localization, and an algorithm for ob-stacle avoidance combining the dynamic window ap-

proach (DWA) with an elastic band method. This re-sults in a very reliable navigation system that is dis-cussed in the following and presented in detail in[3].

5.1. Environment model

Our approach to environment modeling is feature-based using geometric primitives such as lines, seg-ments and points (sometimes called landmarks). Theenvironment topology is encoded in a weighted di-rected graph with nodes and edges between the nodes.Neither for global path planning nor for localizationdo we use a free space model like occupancy grids.The advantages of this choice are:

• Compactness. Indoor maps of this type (featuresplus graph) typically requires around 30 bytes/m2.Further, scaling to 3D is polynomial, whereas gridmaps scale exponentially.

• Filtering. The feature extractor automatically fil-ters out measurement points not belonging towell-defined features.

The graph has two types of nodes:station nodesand via nodes(Fig. 6). Station nodes correspond toapplication-specific (x, y, θ) locations in the plane. Ex-amples from Expo.02 include: showcase with indus-trial robot, tour welcome point, or location to triggerpicture caption. Via nodes have two tasks. First, theycorrespond to topology-relevant locations like doorsor corridor-crossings. Thereby the graph models theenvironment topology. Second, in environments withlarge open spaces, they can help global path planningby defining locations with favorable traversability. Forexample, this was used to optimize the visitor flow byplanning paths to keep the robots far from each other.

The map further contains so-calledghost points.Ghost points are (x, y) positions in the world referenceframe that act as invisible barriers. If the environmentcontains forbidden areas undetectable by the robot’ssensors (e.g. staircases, glass doors, exits, etc.) ghostpoints are used to prevent the robot from this regionsby injecting them into the sensor data as virtual read-ings (see also[11]).

The Expo.02 environment covered a surface of315 m2 and had 17 stations of interest for the robots.The map contained 44 line segments, 17 station nodes,37 via nodes and 20 ghost points, as shown inFig. 6.Its memory requirement was 8 KB, or 26 bytes/m2.


Fig. 6. The Expo.02 map with station nodes (black dots) and via nodes (small circles).

The ghost points (not shown inFig. 6) are at theentrance (bottom ofFig. 6) and the exit (top) of thecirculation area.

5.2. Local path planning and obstacle avoidance

Constraints that must be taken into account whenmoving Robox through the exhibition include theshape, kinematics, and dynamics of the robot on theone hand, and the environment on the other. Thelatter is highly dynamic in a mass exhibition, a factthat strongly influenced the design of obstacle avoid-ance on Robox. Based on previous experience[2] wedeveloped a novel path planning and obstacle avoid-ance algorithm adapted to the challenging exhibitionenvironment[20]. Because purely reactive obstacleavoidance methods tend to have problems with lo-cal minima, a path-planning layer is used to guide areactive method[6,22].

Our sensor-based local path planner combinesan adapted elastic band[21] with the NF1 naviga-tion function [17]. Given a topologically correct butnon-smooth path calculated via NF1, the elastic bandefficiently iterates toward a smooth trajectory thatadapts to changes in the environment. On Robox,elastic band iterations are implemented in a non-timecritical thread running at several hertz. As soon asthe elastic band “snaps”, re-planning is performed inanother dedicated thread. At Expo.02, this occurredapproximately every 10 s.Fig. 7 illustrates such anevent andFig. 8 shows a typical situation at theexhibition.

For local path planning, the robot is assumed to becircular and holonomic, which can be justified by theoctagonal shape of Robox and its ability to turn on thespot. Furthermore, heuristics are used to ignore certainlaser readings (for instance, those that lie within therobot). This results in faster execution.


Fig. 7. Simulation screenshots (which show more information than available from the real robot) illustrate a typical replan sequence (fromleft to right). Laser range data are shown as white dots on black regions. The robot is the octagon, and the goal the white circle. At first,the elastic band is about to snap (gray disks, light gray indicates one of the heuristics). Robox continues to use the snapped band (secondscreenshot, gray curve) and calculates the NF1 in the background (the initial path is the thin black line outlined in white). The initial pathbecomes the new elastic band in the third picture.

The above simplifications are acceptable becausethe geometric, kinematic, and dynamic constraints aretaken into account in the reactive obstacle avoidancelayer, which is a modified version of the DWA[12].Vehicle dynamics are modeled as maximum valuesfor actuator speed and acceleration. Compared to theoriginal DWA, our approach differs in the followingpoints:

• Robox being a differential drive robot, the objec-tive function (which trades off speed, heading, and

Fig. 8. A screenshot from Robox’s supervision web-interface,taken during Expo.02. Such pictures are generated on the robotand help supervising its state. You can see laser data (small dots),lines from the a priori map and lines extracted from the rangedata, the elastic band (circles), and a ghost robot (large dots inthe upper part of the image). The light gray grid shows the scale(resolution of 1 m).

clearance) is calculated in the actuator space (vl, vr)defined by the speedv of the left (l) and right (r)wheel. This models the physical acceleration lim-its of the vehicle more properly than using forwardand rotational speed (v, w) in the Cartesian space.

• As in [22], robot shape is modeled as a polygon. Itis not hard-coded but can be specified at boot time.

• Clearance is defined as time to collision (as opposedto distance). This resolves a singularity when turn-ing on the spot, in which case any collision seems in-stantaneous because the wheel-axle mid-point doesnot move. Using time also causes the algorithm tochoose more clearance at higher speeds.

• Virtual obstacles, such as forbidden zones or knowninvisible objects, are injected as ghost points intothe range readings. They do not have to be treatedspecially by the reactive layer. This concept wasalso used to generate ghost robot contours at theposition of the detected reflector mounted on eachrobot.1

Being part of the safety-critical software of therobot, the dynamic window runs at 10 Hz as adeadline-driven hard real-time task. Special attention

1 Because all robots have their SICK sensors on the same height,they have difficulties seeing each other. This has been solved byadding two reflectors in the blind zone between the two SICKsensors.


was paid to optimize its execution time to be pre-dictable and short. For reasons similar to those in[22],the clearance measure is pre-calculated and stored ina look-up table during startup. Refer to[20] for moretechnical details about the obstacle avoidance used onRobox.

5.3. Localization

Localizing a robot robustly in a mass exhibitionenvironment is certainly a challenge. In former exhi-bition projects, localization was based on off-boardhardware[7,24] or environment modifications[19].In our earlier work we employed features and anExtended Kalman Filter(EKF) [4]. This is also theapproach for the three museum robots described in[13]. However, a robot doing (single-hypothesis) posetracking can lose its track as the inherent data asso-ciation problem is ignored. With our new localizationtechnique introduced in[1], we address the data asso-ciation problem and extend the conventional EKF lo-calization approach to a global localization technique.

Unlike POMDP or Markov approaches[7,24]wherelocations2 are generated before they get evaluated bythe exteroceptive sensors (as a grid or as particles),our approach to localization turns this process around:locations are generated as a direct consequence of sen-sory information. Features tell uswhenandwheretoplace a location hypothesis. This allows us to alwaysmaintain as many hypotheses as necessary and as fewas possible.

The technique for hypothesis generation is aconstrained-based search in an interpretation tree[8,9,14]. This tree is spanned by all possible local-to-global associations, given a local map of observedfeaturesL = {li}pi=1 and a global map of model fea-turesG = {gj}mj=1. Besides track formation, we haveshown an algorithm for track splitting under geomet-ric constraints. It relies on the same idea as hypothesisgeneration (search in an interpretation tree), formingthus a consistent framework for global EKF localiza-tion. It is beyond the scope of this paper to treat theframework in detail. We will therefore focus on themain ideas and results. Please refer to[1,8] for a more

2 We use the terms location, position and pose interchangeably.They denote all the full (x, y, θ) vehicle pose.

complete presentation andFig. 9 for an example ofhypothesis generation in the exhibition environment.

5.3.1. Hypotheses generationThe search space for hypothesis generation is the

space of all possible associations of the observedfeatures li and the model featuresgj. The spacehas the structure of a tree withp levels andm + 1branches[14]. p is the number of observed fea-tures in L, m the number of model feature inG.The extra branch (called star branch) allows correctassociations in the presence of outlier observations(false positives) and thus accounts for environmentdynamics and map errors. During tree traversal, sta-tistically feasiblepairings pij = {lj, gj} are soughtgiven all uncertainties associated to the features.A pairing says that the observed featureli and themodel featuregj denote the same physical objectin the environment (gj is called an interpretation ofli). Geometric constraints from the features are ap-plied to the formation of pairings. They determinetheir statistical compatibility on a significance levelα. Although the problem is of exponential complex-ity, the geometric constraints reduce enormously thespace to be explored and can be classified into twocategories:

1. Location independent constraints:• Unary constraint. We accept the pairingpij if li

andgj are of the same type, color, size or anyother intrinsic property. Examples: the length ofthe observed segmentli is equal (or smaller) thanthe length of the model segmentgj.

• Binary constraint. Given a valid pairingpij wewill accept the pairingpkl only if the two localfeaturesli andlk are compatible to the two globalfeaturesgj andgl. Examples:li and lk are lineswith the intermediate angleϕik. Then, the pairingpkl is considered compatible ifϕik = ϕjl . Withpoint features, for instance, the distancesli − lkandgj − gl must correspond.

2. Location dependent constraints: The above testsdo not involve the robot positionLh. Once this isknown, a further class of constraints can be applied.• Visibility constraint. This constraint only applies

to features from the map. It tests whethergj

is visible from the robot positionLh. Example:lines or segments can be seen only from one


Fig. 9. Hypothesis generation. Given the local maps in (a) and (b), hypotheses are generated at locations where the local map ‘fits’ intothe global map. In (a) there are 15 hypotheses (with their 95% error ellipse); the location is ambiguous. In (b) there is a single hypothesis;the robot is instantaneously localized.t denotes the execution time.

side. If the robot is behind a wall, one of thetwo lines modeling the wall is invisible. Withsensor-specific parameters, the visibility con-straint rejects features, which are not detectable,for instance, because they are farther than amaximal perception radius.

• Rigidity constraint. A pairing pij is consideredcompatible if li and gj, transformed into thesame coordinate system givenLh, coincide (areat the same position). This is what happens inthe matching step of any EKF localization cycle.Usually,gj is transformed into the frame ofli.


• Extension constraint. A pairingpij is consideredcompatible ifli andgj, transformed into the samecoordinate system givenLh, fully overlap. Exam-ple: an observed segmentli must be completelycontained in the transformedgj seen from thelocationLh.

5.3.2. The search algorithmThe constraints allow the discard of whole sub-

spaces (subtrees) from the search each time an incom-patible pairing is found at the root of such a subtree.With the uncertainties associated to local and globalfeatures, all decisions make use of the Mahalanobisdistance on a significance levelα.

Tree traversal is implemented as a recursiveback-tracking search algorithmgeneratehypothesesdescribed in[1,8]. The strategy is to first find aminimal number of valid pairings with location in-dependent constraints such that a location estimatecan be determined in order to apply location depen-dent constraints, too. Each time when the algorithmreaches the bottom of the tree (that is, all observedfeatures have been successfully assigned to a modelfeature or to the star branch) we have a valid robotlocation hypothesis. The pairings which supportthe hypothesis are put together in asupporting setSh = {{l1, gj1}, {l2, gj2}, . . . , {lp, gjp}} and therebyconstitute a location hypothesish = {Sh, Lh}. Allhypotheses together form the set of robot locationhypothesesH = {hi}ni=1.

5.3.3. Estimating the robot location from Sh

With the supporting set, the (x, y, θ) pose of therobot is not yet known. This is what theExtendedInformation Filter (EIF) does. Given a supportingset with all associated uncertainties, it estimatesthe robot location and its covariance in the leastsquare sense. The difference between the EIF andthe EKF is that the former is the batch estimatorformulation of the latter (which is recursive). Thisis needed for hypothesis generation, because there isno a priori knowledge on the robot location whichmeans formally that the state prediction covariance,usually calledP(k|k + 1), is infinite. With the EIF,this can be properly expressed asP−1(k + 1|k) =03×3 since covariance matrices are represented inthe information matrix form, that is, by their in-verse.

6. Interaction and emotional expressions

Human–robot interaction with several robots in apublic mass exhibition is a complex task. The goalof the Robox installation at Expo.02 was to allow thevisitor a unique experience in sharing the environ-ment with intelligent mobile robots and to interact withthem. Furthermore, the robots have to guide the visi-tors around and explain part of the Robotics exhibition.So the robots are content (personal robot technology)and medium (tour guide) at the same time. On onehand, the interaction should be as intense and fascinat-ing as possible in order to enable a rich visitor experi-ence with the current state-of-the-art mobile roboticstechnology. On the other hand, as tour guides, therobots have to maximize the visitor and informationflow, thus should restrict the visitor interaction. Thesecontradictory goals imposed various restrictions on theexhibition and resulted in a continuous evolution of thescenarios throughout the duration of the exhibition.

The central object for controlling the interaction andthe behavior of the robots is theScenario Object Util-ity Language(SOUL) [16]. It is used to implementthe different scenarios using all the input and outputchannels available on Robox.

6.1. SOUL

SOUL aims at composing the scenarios like a the-ater or a music composition. Through a convenientgraphical interface it allows to combine different basicbehaviors with synthesized speech, motion, sensoryinputs and much more, and to supervise its execution(Fig. 10). The block diagram of the objects integratedin SOUL is shown inFig. 11.

In general, we distinguish between static scenar-ios that are usually a fixed sequence of the tour, anddynamic scenarios that can be considered behaviorstriggered by special events, e.g. if the visitors areblocking the way of the robot. If a dynamic scenariois triggered, SOUL will interrupt the current staticscenario and execute a corresponding exception sce-nario telling the visitor that it is aware of his actions,before resuming the tour. In this sense, dynamic sce-narios are more appealing for a lot of visitors, becausethey demonstrate awareness of the robot.

The main input and output channels available onRobox for composing the scenarios are:


Fig. 10. Graphical interface of SOUL, allowing for convenient composition (left) and supervision (right) of scenarios.

• Input channels◦ Robot location and state (goal reached/not

reached).◦ Blocked pathway (from obstacle avoidance mod-

ule).◦ Bumper contact (eight bumpers surrounding the

robot).◦ Four input buttons for visitor interaction.◦ Face tracking [15,16] (through eye camera,

Fig. 13).◦ People-motion tracking[16] (through laser sen-

sor,Fig. 14b).◦ Speech input (only on two robots).◦ Other robots’ locations (through multi-robot co-

ordination system).◦ Emergency button.◦ Battery status.◦ Remote intervention through supervisor.

• Output channels◦ Output of synthesized speech in English, French,

German, and Italian using Mbrola[10] andLAIPTTS [23]. The pitch, rate and volume ofthe speech output can be selected directly in theSOUL interface.

◦ Robot motion (definition of next goal location,expressional motions).

◦ Illumination of four input buttons (e.g. green for“yes”, red for “no”, or different colors for lan-guage selection).

◦ Facial expressions composed using the eye andeyebrows motion and the LED matrix display(Fig. 12). The following seven basic expressionsare used: sadness, disgust, joy, anger, surprise,fear, and calm[18].

◦ Direct use of icons and animations on the LEDmatrix display in the eye.

◦ Control of environment through domotic system.

6.2. People tracking

To detect visitors we relied on two sensors and ded-icated algorithms. The laser scanner data is fed to amotion detector, indicating where in the robot’s vicin-ity movement is present. Color images from the cam-era in the robot’s left eye are used to identify skin colorand subsequently human faces using heuristic filters.

6.2.1. Motion detectionMotion detection[16] distinguishes between mov-

ing and static elements of the environment. Thedetection of moving elements is possible becausethey change the range readings over time. During


Fig. 11. Block diagram of the basic objects and connections inte-grated in SOUL.

initialization, we assume that the environment is staticand convex. The range readings are integrated in theso-called static map, consisting of all currently vis-ible non-moving elements. In contrast to grid-basedapproaches, we are working in the polar coordinatesof the laser range sensor. For the available angularresolution from the laser (1◦ or 0.5◦) this results in apolar map size of 360 or 720 elements, respectively.

In the next step, we compare the new informationfrom the range finder with the static map. Assuming a

Fig. 12. Three facial expressions. From left to right: happy, sur-prised and angry.

Gaussian distribution of the sensor readings represent-ing a given element, we use aχ2 test to decide whetherthe current reading belongs to one of the elements ofthe static map or originates from another element, inwhich case the latter is then classified as dynamic.

All readings classified as static are used to updatethe static map. Readings labeled as dynamic are usedto validate the map. Validation is performed as follows:if a reading labeled as dynamic is closer to the robotthan the corresponding value from the static map, thelatter persists. If it is farther away than the map value,it is used to update the map, but remains labeled asdynamic.

All dynamic elements are clustered according totheir spatial location. Each cluster is assigned a uniqueID and its center of gravity is computed.

The classification, update, and validation steps arerepeated for every new scan. In case of robot motion,the process becomes slightly more complicated, sincethe static map has to be warped to the new robot po-sition. This is possible knowing the displacement ofthe robot.

A result of the motion detection is shown inFig. 14,where information from all active robots combinedprovides a snapshot of the exposition and the positionof the robots and visitors. There are 140 motion ele-ments approximating the number of visitors present.However, since the laser sensors are mounted at theheight of the knees, some persons might be repre-sented by two motion elements, each correspondingto one leg.

6.2.2. Face detectionInformation gathered from face tracking is used in

several parts. Together with the motion detection, ithelps to verify the presence of visitors. Using visualservoing, the robot can look at the person it is interact-ing with. The main steps of face detection and trackingare[15,16]:

• Skin color detection. Among the different colorspaces we chose the RGB space. Green and bluevalues are normalized using the red channel. Thispartially overcomes problems with in illuminationchanges. Fixed ranges for blue, green and bright-ness values are accepted as skin color. Erosion anddilation are performed on the resulting binary im-age to remove small regions.


Fig. 13. Visitors at the Expo.02 seen from the robot. Skin colored regions show a light border. Dark borders indicate clusters that passed theheuristic filters. The face tracking algorithm is able to maintain the visitor IDs even with a moving camera and resulting out of focus images.

• Contour extraction and filtering. The binary imageis clustered and the contour of each cluster is ex-tracted. We apply heuristic filters to suppress skincolor regions that are not faces. These filters arebased on rectangular areas, their aspect ratio, andthe percentage of skin color. Another filter we ap-ply is based on the morphology of the skin color re-gion. We expect holes for eyes or the mouth withinthe region. This reduces the cases where the robotis looking at hands or other body parts.

• Tracking. The system tries to update the positionsof already tracked regions based on the position ofclusters in the current image. Clusters that remainunassigned to previous tracks are added and trackeduntil they leave the camera’s field of view (Fig. 13).

6.3. Composition of scenarios

Fig. 14a depicts the layout of the exhibition. Pre-sentation stations are defined near particular objectsin the exhibitions. Presentation stations may compriseseveral goal nodes, as is the case for the welcomepoint, thus tours can start simultaneously. Fourteenpresentation stations were located all over the exposi-tion space (Fig. 14a). Finally, there are goodbye sta-tions close to the exit. Each station corresponds to onescenario in the SOUL system, providing visitors withthe necessary explanatory or entertaining information.Tours can be created by a succession of several pre-sentation stations. Two stationsphotoandpoet robot

are not included in any of the tours and are perma-nently occupied with a dedicated robot. The remain-ing nine tour-guide robots can reach all the other sta-tions. In order to avoid having several robots present-ing at the same goal node, each robot can ask theposition of all the other robots at any time. This al-lows each robot to dynamically select the next goal,based on visitors’ preferences and availability of thestations.

6.4. Control of the visitor flow

Expo.02 was a mass exhibition with several thou-sand visitors per day. The Robotics exhibition hostedaround 500 visitors per hour that, in average, stayedaround 15 minutes in the interactive zone of the ex-hibition. This results in around 125 visitors which areat the same time enjoying and interacting with therobots. In order to best serve the visitor and to controlthe visitor flow from the entry area to the exit, the tourstarts at the welcome station and will guide the visitoralways closer to the exit.

Visitor flow is channeled by two factors. First, thenumber of stations the robot visits before going to thegoodbye station is limited to five. Second, the guidedexposition tour will always lead visitors closer to theexit. This eases navigation and helps to maintain thevisitor flow. Technically this is realized by a list ofpossible next presentation stations. Each presenta-tion scenario has an individual list, containing only


Fig. 14. (a) Scheme of the 315 m2 exhibition area with the presentation stations of the tour. (b) Snapshot of the exposition with sevenrobots (black dots) and the visitors as (point-clusters) found by the motion detection algorithm.

stations that support the direction of the main visi-tor flow. When requesting exposition state from theglobal supervisor, the robot will seek only stationsthat it has not yet visited, and that are closer to theexit than it currently is. In order to further ensureproper handling of visitor density and flow, four basicexhibition modes were introduced:

• Wait for Visitor. If only few visitors are in the exhi-bition, the robots wait for one to come close enoughbefore starting to talk and ask them which stationthey would like to see.

• Visitor’s Choice. If the exhibition is at its regularvisitor capacity, the robot will ask at each stationwhether the visitor wants to go to a next station.

• Robot’s Choice. If the visitor density is very high,the robot will decide what the next station is and gothere without asking.

• No Move. If too many visitors are in the exhibitionarea, the robot will not be able to move any more,and thus stays with one station and presents it per-manently.

In order to run theVisitor’s Choicemode, whichwas the regular case, the staff at the entrance of theexhibition controls the number of entering visitors.

7. Results

By October 20, 2002, after 159 days of operation,the 11 Robox accumulated:

• 13,313 hours of operational time,• 9415 hours of motion,• 3315 km travel distance,• 686,000 visitors.

This is to our knowledge the biggest installation ofinteractive personal robots to date.

7.1. Reliability and performance

Starting with the opening of the exhibition on May15, 2002, the robots operated fully autonomously, 7days a week, 10.5 hours a day at the beginning, then11 hours a day and 12 hours for the last 3 weeks. Dueto some delays in the development, the software wasstill in the test phase during the first weeks of the ex-hibition. Therefore, we started with aMean Time Be-tween Failure(MTBF) of less than 1 hour. The MTBFexcluding the first 4 weeks of the exhibition was 8.6hours, which results in an average of one problemper robot per day. The large majority of failures were


caused by software problems in the interaction sys-tem (PC running Windows 2000) and were thereforenot safety-critical. The navigation system (PowerPCrunning real-time system XO/2) had since the begin-ning a MTBF in the range 20–40 hours and stabilizedto around 60 hours. The robot hardware, apart fromsome problems with the amplifiers for the drive mo-tors, was highly reliable from the beginning. We didnot encounter problems with serious vandalism. How-ever, visitors seemed to be strongly attracted by thefoam bumpers and the emergency button that werevery frequently tested by them.

7.1.1. SafetyIt was never observed that a robot was the cause

of a dangerous situation (e.g. with small children, el-derly or handicapped people). Collisions occurred butwere typically provoked by the visitors themselves.The lack of additional sensors close to the floor (IRor ultrasonic), like the robot in[7], was bearable withthe combination of tactile plates and foam bumpers.Blocked situations due to bumper contact could alsobe handled by the interaction system (robot express-ing friendly menaces).

7.1.2. Performance of navigation systemThe division of obstacle avoidance into a purely

reactive part with high model fidelity and a planningpart with local scope is a powerful conjunction. Veryoften, groups of visitors formed a U-shaped obstacleor left only small ‘holes’ for passage. The robots hadno difficulty to escape from such situations, and dueto the fact that we accounted for Robox true octag-onal shape, narrow passages were efficiently used.Further, the elastic band generates good-looking tra-jectories. Our approach to the robot-sees-robot prob-lem works in most situations. However, generating aghost robot contour at the detected reflector positionis an oversimplification in certain situations, as theghost robot is larger than the real one, and robotssometimes stay mutually blocked even though thereis sufficient space to maneuver. Such deadlocks oc-cur most frequently when two robots approach eachother head-on because obstacle speed is currently nottaken into account in the obstacle avoidance method,and by the time the robots come to standstill, eachperceives the other as being partly inside its out-line.

The reliability of localization was a surprise inview of the environment dynamics and the fact thatwe used a horizontally scanning laser sensor only. Afallback solution with lamp features extracted by acamera looking to the ceiling was prepared but neverused. However, lost situations occurred, their reasonsbeing (in decreasing frequency): (i) pavilion staffmembers that push/rotate the robot in order to un-tangle congestions of visitors and robots, (ii) visitorsimitating this behavior, (iii) unknown cause and (iv)failure of another component (hardware/software).Global localization as described inSection 5.3wasthen very useful. Often it was possible to instantlyrelocalize within all people, enabling it to resumeoperation. The specific geometry of the Expo.02 en-vironment was helpful here since it contained littlesymmetry.

Localization accuracy varies and depends on howmuch persons occlude the laser sensors and how muchsegments have been paired. Goal nodes were typicallyreached with a relative error less than or equal to 1 cm.This was measured by the traces on the floor at goallocations left by the robots after several months of op-eration. From a practicability point of view, localiza-tion was implemented as a non-real-time thread. Itscycle time was slowed down to 2 Hz in favor of otherconcurrent non-real-time processes (mainly communi-cation). With a single hypothesis to be tracked, cycletimes of 10 Hz and more are achievable on the Roboxhardware.

Besides the specific qualities of our approach de-scribed so far, we believe that the use of geometricfeatures for navigation is also an appropriate choicein highly dynamic environments. During feature ex-traction, sensor readings are sought which satisfy aspatial model assumption (e.g. from a line, a cor-ner, or a ceiling light). Thereby, the extraction pro-cess acts as a filter saying which reading is to betaken for localization and which one is to be ig-nored. This filter relies typically on sound regressiontechniques and works independently on whether therobot is localized or not (as opposed to the ‘dis-tance filter’ in [24]). A group of people standingaround the robot does not produce evidence for theline extraction. Spurious segments, for example, online-like objects carried by people, can occur and aretreated by the star-branch in the localization algo-rithm.


7.1.3. Performance of interaction systemThe final interaction software SOUL was oper-

ational since July 1, 2002. The exhibition modeVisitor’s Choicewas active approximately 95% of thetime, the modeRobot’s Choiceduring the remaining5%. We experienced around 20 days with more than5000 visitors. Even in this crowded environment,robots managed to move to their goal in a reasonabletime, so that the modeNo Move was never used.The modeWait for Visitor was never used, since therobots were most of the time surrounded by inter-ested visitors anyway. With five stand-by scenarios,robots ran out of those scenarios less than once aweek.

Visitors stayed between 10 and 45 minutes with therobots. We tried to control this by changing the tourlength from two to ten stations without noticing an im-pact on the visitor’s stay. People just moved on to thenext robot or even stayed with the current one. Here,enhanced environmental information, like motion in-formation of the visitor or face recognition might helpcreating more convincing scenarios. We found thatvisitors quit a robot approximately after four stations,which was the actual tour length. The average numberof visitors after installing the global supervisor (visi-tor flow control and robot coordination) rose slightlyto around 4600 per day. This makes it hard to provea quantitative effect on the visitor flow. However, ob-servation of the crowd shows that visitors appreciatedhaving the choice to go to a station. This adds a littleinteractive element to the tour.

7.1.4. Visitors’ experience and feedbackThe visitor’s experience is in general very positive,

with more than 83% of the visitors rating the exhi-bition as very good or good and less than 5% of thevisitors rating the exhibition as bad. However, we en-countered various problems with the first concept ofthe exhibition:

• Guiding visitors at public exhibition by a robot issomewhat difficult, because playing and interactingwith the robot seems to be more attracting to thevisitors. Some visitors were also not very patientand not willing to follow the instructions of therobot. However, even if a lot of people did not followa whole tour of the robots, most of them had anexciting first contact with a personal robot.

• Due to the large number of visitors and robots shar-ing the exhibition hall, it was sometimes difficult tounderstand the artificial voice of the robot.

• The basic goal of the exhibition was to experi-ence the increasing closeness between man andmachine and not to present technical details ofthe robot. However, plenty of visitors were eagerto get some insight on the robots. We thereforeadded a station at which the robot was presentinghis functionalities with the help of a PowerPointpresentation.

• Some visitors were disappointed about the perfor-mance and intelligence of the robot. Their overratedexpectations are mainly coming from science fic-tion movies and popular science presentations notreally reflecting the state-of-the-art.

In order to quantify the visitors’ appreciation andperception of the exhibition we made two public in-quiries. A first questionnaire answered by 2043 visi-tors at the exit of the exhibition focused on the socialimpact and future of robotics technology in a largesense and is not discussed here. A second question-naire filled in by 206 visitors concentrated on the gen-eral appreciation and quality of the interaction. Someprincipal results are summarized below:

• In a first question the visitors were asked to judgeon the general experience, like amusement valueor interactivity of the robot. Multiple answerswere possible. 60% of the visitors judged the ex-hibition interesting and 40% amusing, and only4% were bored by the exhibition. However, onlyabout 12% of the visitors perceived the robots asreally interactive. This might be due to the factthat we had to limit the interactivity in order to

Fig. 15. Visitors’ feedback on the robot’s character.


reach a reasonable visitor flow. However, naturaland easy to understand multi-modal interactionis still an open research issue and need furtherinvestigation.

• The functionality of the four input buttons was eas-ily learned by 66% of the visitors through their in-teraction with the robot, 21% learned by imitationof other visitors. Only about 13% needed supportfrom the staff or did not understand the functional-ities at all.

• Around 75% of the people were able to follow thespoken explanations of the robot very well. This isalso verified by the fact that 76% of the visitorslearned from the robot that it is using a laser fornavigation and detection of its environment.

• The appearance and character of the robot was ap-preciated very much by the visitors (seeFig. 15).This might also explain the fact that over 70% of

Fig. 16. The robot doing its job in the exhibition. The bright spot on the robot is the reflector mounted so that the robots can see each other.

the people would not hesitate to ask the robot forinformation or help if it would offer his service ina supermarket or railway station.

8. Conclusion

The scale and duration of the Expo.02 project wasa unique opportunity to validate techniques and togather long-term experience on various levels. Theframework presented in this paper meets all ini-tial requirements. We described the basic hardwareand software concept of Robox and discussed thenavigation and interaction system. The results after159 days of operation are very positive and provethe feasibility of the selected concepts, hardware,software and algorithms. The presented exhibitionrobot project with 11 fully autonomous robots, 5


months of operation and 686,000 visitors enabledus to improve our current technology and to learnthrough an outstanding real-world research platform(Fig. 16).

Acknowledgements

The presented project is the result of a very strongteam effort. Apart from the core team co-authoringthis paper, various people from academia and indus-try supported the project. The project was funded byExpo.02 and EPFL. BlueBotics, a spin-off of our Lab,realized the production of the 11 robots.

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Roland Siegwart is a director of the Au-tonomous Systems Lab (ASL) at the SwissFederal Institute of Technology Lausanne(EPFL). He received his Master’s degreein Mechanical Engineering in 1983 andhis Ph.D. in 1989 at the Swiss FederalInstitute of Technology Zurich (ETH). In1989/1990 he spent one year as postdoc atStanford University. From 1991 to 1996he worked part time as R&D director at

MECOS Traxler AG and as a lecturer and deputy head at the In-stitute of Robotics, ETH. In 1996 he joined EPFL as full professorwhere he is working in robotics and mechatronics, namely mo-bile robot navigation, space robotics, human–robot interaction, allterrain locomotion and micro-robotics. He is a member of variousscientific committees and cofounder of several spin-off companies.

Kai Oliver Arras is a Ph.D. student withthe Autonomous Systems Lab (ASL) atthe Swiss Federal Institute of Technol-ogy Lausanne (EPFL). He received hisMaster’s degree in Electrical Engineeringfrom the Swiss Federal Institute of Tech-nology Zurich (ETHZ) in 1995 and workedas a research assistant in Nanorobotics atthe Institute of Robotics in Zurich. In 1996he joined Prof. Siegwart to help setting up

the Autonomous Systems Lab at EPFL, where he is working onseveral aspects of mobile robotics. His fields of interest includesystem integration, feature extraction, local and global localiza-tion, SLAM, robot art, and robots in exhibitions.

Samir Bouabdallah is research assistantand Ph.D. student at the Autonomous Sys-tems Lab (ASL) at the Swiss Federal Insti-tute of Technology Lausanne (EPFL). Hegot his Master’s degree in Electrical En-gineering from Abu Bakr Belkaid Univer-sity (ABBU), Tlemcen, Algeria, in 2001.His master thesis was the development ofan autonomous mobile robot for academicresearch. His current research interests are

control systems for all terrain locomotion and flying robots.

Daniel Burnier is manager of the elec-tronics workshop of the AutonomousSystems Lab (ASL) at the Swiss FederalInstitute of Technology Lausanne (EPFL).He received his Diploma in ElectricalEngineering from the University of Ap-plied Science of Yverdon in 1986. Until1988 he worked at the same university. In1988 he joined the company CYBELECwhere he worked as R&D engineer on

the digital control of machine tools. After 5 years as medicalauxiliary he joined the Autonomous Systems Lab in 2000, wherehe is involved in the development of mobile robots and managesvarious development projects.

Gilles Froidevaux studied Microengi-neering at the University of Neuchateland at the Swiss Federal Institute of Tech-nology Lausanne (EPFL) and receivedhis Master’s degree in 2001. His masterthesis was the mechanical design andcontrol of a robot arm. In spring 2001, hejoined the Expo.02 robotics team at EPFLto work on the human–robot interactionwith a main focus on face tracking. After

the successful completion of the Expo.02 project he founded theengineering company FiveCo—Innovative Engineering togetherwith colleagues of the project team.

Xavier Greppin received his Master’s de-gree in Microengineering from the SwissFederal Institute of Technology Lausanne(EPFL) in 2001. During his master thesishe developed a small mobile robot withwireless control. In spring 2001 he joinedthe Expo.02 robotics team at EPFL towork on the human–robot interaction witha main focus on the design of realizationof the face and its control. After the suc-

cessful completion of the Expo.02 project he founded the engi-neering company FiveCo—Innovative Engineering together withcolleagues of the project team.

Björn Jensen is a Ph.D. student atthe Autonomous Systems Lab (ASL) atthe Swiss Federal Institute of Technol-ogy Lausanne (EPFL). He received hisMaster’s degree in Electrical Engineer-ing and Business Administration fromthe Technical University of Darmstadt,Germany in 1999. His main interest is inenhancing man–machine communicationusing probabilistic algorithms for feature

extraction, data association, tracking and scene interpretation.


Antoine Lorotte decided to leave Paristo study at the Swiss Federal Institute ofTechnology Lausanne (EPFL), where hegot his Master’s degree in Microengineer-ing in 2001. During his masters projectat the Autonomous System Lab (ASL)he developed a complete system of homeautomation (domotic). In spring 2001 hejoined the Expo.02 robotics team at EPFLto work on the human–robot interaction

with a main focus on the Scenario Object Utility Language usedon the Robox. After the successful completion of the Expo.02project he founded the engineering company FiveCo—InnovativeEngineering together with colleagues of the project team.

Laetitia Mayor studied at EPFL andCarnegie Mellon University and receivedher Master’s degree in Microengineer-ing from the Swiss Federal Institute ofTechnology Lausanne (EPFL) in 2002. Inher master thesis she developed a con-cept for emotional human–robot interac-tion. In spring 2002 she joined the Expo.02robotics team at EPFL to work on emo-tional human–robot interaction and the de-

velopment of scenarios. After the successful completion of theExpo.02 project she joined the company SYNOVA, specialized inlaser cutting.

Mathieu Meisser was born in Ticino,southern Switzerland. After college he de-cided to move to Lausanne to study Mi-croengineering and received his Master’sdegree in 2001. In his master project hedeveloped a small mobile robot with wire-less control. In spring 2001 he joined theExpo.02 robotics team at EPFL to work onthe human–robot interaction with a mainfocus on the speech synthesis. After the

successful completion of the Expo.02 project he founded the en-gineering company FiveCo—Innovative Engineering together withcolleagues of the project team.

Roland Philippsen received his Master’sdegree in Microengineering in 2000 atthe Swiss Federal Institute of Technol-ogy Lausanne (EPFL). During his masterthesis at Stanford University he analyzedthe material an information flow throughthe partially automated DNA sequencing.He is currently working on his Ph.D. inpath planning and obstacle avoidance formobile indoor robots at the AutonomousSystems Lab (ASL).

Ralph Piguet is the head of the electron-ics workshop of the Autonomous SystemsLab (ASL) at the Swiss Federal Institute ofTechnology Lausanne (EPFL) since 1997.He received his Diploma in Electrical En-gineering from the University of AppliedScience of Lausanne in 1996. From 1992to 1997 he worked with Bobst SA in Lau-sanne. During this period, he was involvedin the development of imaging devices and

quality control systems as well as with the training of appren-tices. Since 1997 he is involved in various developments of mo-bile robots. He is also executive board member of the spin-offcompany BlueBotics that realized the exhibition robots.

Guy Ramel is native of Geneva. After fin-ishing the technical school in electronicand the Engineer School of Geneva (EIG)he studied physics at the Swiss FederalInstitute of Technology Lausanne (EPFL).He received this diploma in physics (2001)with a work in astrophysics and an en-gineer project in neural networks. He isactually pursuing a Ph.D. in the field ofmulti-modal interaction and human–robot

interactions at the Autonomous Systems Lab (ASL) at the SwissFederal Institute of Technology Lausanne (EPFL).

Gregoire Terrien received his Master’sdegree in Microengineering in 2000 fromthe Swiss Federal Institute of TechnologyLausanne (EPFL). He did his master the-sis at Carnegie Mellon University (CMU)in the field of mobile robots teleopera-tion based on sensor fusion. After that hestarted to worked a the Autonomous Sys-tems Lab (ASL) at EPFL, and since 2001he holds also a part-time position as ex-

ecutive board member with BlueBotics SA, a spin-off companyof the ASL. He is involved in different projects from mechanicaland electrical design to low-level and high-level programming.

Nicola Tomatis received his Master’s de-gree in Computer Science in 1998 fromthe Swiss Federal Institute of TechnologyZurich (ETH). After this, he moved to theSwiss Federal Institute of Technology Lau-sanne (EPFL), where he received his Ph.D.in 2001. His research covered metric andtopological (hybrid) mobile robot naviga-tion, computer vision and sensor data fu-sion. Since autumn 2001 he holds a part

time position as senior researcher with the Autonomous SystemsLab, and is executive board member with BlueBotics SA spin-off,which is involved in mobile robotics.

robox at expo.02: a large-scale installation of personal...

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