The Interactive Museum Tour-Guide Robot .The Interactive Museum Tour-Guide Robot Wolfram Burgard,

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  • The Interactive Museum Tour-Guide Robot

    Wolfram Burgard, Armin B. Cremers, Dieter Fox, Dirk Hahnel,Gerhard Lakemeyery, Dirk Schulz, Walter Steiner, and Sebastian Thrunz

    Computer Science Department III yComputer Science Department zSchool of Computer ScienceUniversity of Bonn Aachen University of Technology Carnegie Mellon University

    Bonn, Germany Aachen, Germany Pittsburgh, PA


    This paper describes the software architecture of an au-tonomous tour-guide/tutor robot. This robot was recentlydeployed in the Deutsches Museum Bonn, were it guidedhundreds of visitors through the museum during a six-daydeployment period. The robots control software integrateslow-level probabilistic reasoning with high-level problemsolving embedded in first order logic. A collection of soft-ware innovations, described in this paper, enabled the robotto navigate at high speeds through dense crowds, while re-liably avoiding collisions with obstaclessome of whichcould not even be perceived. Also described in this paperis a user interface tailored towards non-expert users, whichwas essential for the robots success in the museum. Basedon these experiences, this paper argues that time is ripe forthe development of AI-based commercial service robots thatassist people in everyday life.

    IntroductionBuilding autonomous robots that assist people in every-

    day life has been a long-standing goal of research in ar-tificial intelligence and robotics. This paper describes anautonomous mobile robot, called RHINO, which has re-cently been deployed at the Deutsches Museum in Bonn,Germany. The robots primary task was to provide interac-tive tours to visitors of the museum. In addition, the robotenabled people all around the world to establish a virtualpresence in the museum, using a Web interface throughwhich they could watch the robot operate and send it tospecific target locations.

    The application domain posed a variety of challenges,which we did not face in our previous research, carried outmostly in office-like buildings. The two primary challengeswere (1) navigating safely and reliably through crowds, and(2) interacting with people in an intuitive and appealingway.

    1. Safe and reliable navigation. It was of ultimate impor-tance that the robot navigated at approximate walking

    Copyright 1998, American Association for Artificial Intelli-gence ( All rights reserved.

    speed, while not colliding with any of the various ob-stacles (exhibits, people). Three aspects made this prob-lem difficult. First, RHINO often had to navigate throughdense crowds of people (c.f. Figures 1 and 2). People of-ten blocked virtually all sensors of the robot for extendeddurations of time. Second, various exhibits were invis-ible to the robot, that is, the robot could not perceivethem with its sensors. This problem existed despite thefact that our robot possessed four state-of-the-art sensorsystems (laser, sonar, infrared, and tactile). Invisible ob-stacles included glass cages put up to protect exhibits,metal bars at various heights, and metal plates on whichexhibits were placed (c.f., the glass cage labeled o1and the control panel labeled o2 in Figure 3). Navigat-ing safely was particularly important since many of theexhibits in the museum were rather expensive and easilydamaged by the 300 pound machine. Third, the configu-ration of the environment changed frequently. For exam-ple, the museum possessed a large number of stools, andpeople tended to leave them behind at random places,sometimes blocking entire passages. Museum visitorsoften tried to trick the robot into an area beyond itsintended operational range, where unmodeled and invis-ible hazards existed, such as staircases. For the robot tobe successful, it had to have the ability to quickly detectsuch situations and to find new paths to exhibits if possi-ble.

    2. Intuitive and appealing user interaction. Tutoringrobots, by definition, interact with people. Visitors ofthe museum were between 2 and 80 years old. The av-erage visitor had no prior exposure to robotics or AI andspent less than 15 minutes with the robot, in which he/shewas unable to gain any technical understanding whatso-ever. Thus, a key challenge was to design a robot that wasintuitive and user friendly. This challenge included thedesign of easy-to-use interfaces (both for real visitors andfor the Web), as well as finding mechanisms to commu-nicate intent to people. RHINOs user interfaces were animportant component in its practical success.

    The specific application domain was chosen for two pri-

    From: AAAI-98 Proceedings. Copyright 1998, AAAI ( All rights reserved.

  • RHINO o1


    Laser rangefinder




    Stereo camera

    Fig. 1: RHINO gives a tour Fig. 2: Interaction with people Fig. 3: The Robot and its sensors.

    mary reasons. First, we believe that these two challenges,safe navigation and easy-to-use interfaces, are prototypicalfor a large range of upcoming service robot applications.Second, installinga robot in a museum gave us a unique andexciting opportunity to bridge the gap between academicresearch and the public.

    This paper describes the key components of RHINOssoftware architecture. The software performs, in real-time,tasks such as collision avoidance, mapping, localization,path planning, mission planning, and user interface control.The overall software architecture consists of approximately25 independent modules (processes), which were executedin parallel on three on-board PCs and three off-board SUNworkstations, connected via a tetherless Ethernet bridge(Thrun et al. 1998a). The software modules communicateusing TCX (Fedor 1993), a decentralized communicationprotocol for point-to-point socket communication.

    RHINOs control software applies several design princi-ples, the most important of which are:

    1. Probabilistic representations, reasoning, and learn-ing. Since perception is inherently inaccurate and incom-plete, and since environments such as museums changefrequently in rather unpredictable ways, RHINO per-vasively employs probabilistic mechanisms to representstate and reason about change of state.

    2. Resource adaptability. Several resource-intensive soft-ware components, such as the motion planner or the lo-calization module, can adapt themselves to the availablecomputational resources. The more processing time isavailable, the better and more accurate are the results.

    3. Distributed, asynchronous processing with decentral-ized decision making. RHINOs software is highly dis-tributed. There is no centralized clock to synchronize thedifferent models, and the robot can function even if ma-jor parts of its software fail or are temporarily unavailable(e.g., due to radio link failure).

    The remainder of this paper will describe those softwaremodules that were most essential to RHINOs success. In

    particular, it will present the major navigation componentsand the interactive components of the robots.

    NavigationRHINOs navigation modules performed two basic func-tions: perception and control. The primary perceptual mod-ules were concerned with localization (estimating a robotslocation) and mapping (estimating the locations of the sur-rounding obstacles). The primary control components per-formed real-time collision avoidance, path planning, andmission planning.


    Localization is the problem of estimating a robots coordi-nates (also called: pose or location) in x-y- space, wherex and y are the coordinates of the robot in a 2D Cartesiancoordinate system, and is its orientation. The problem oflocalization is generally regarded to be difficult (Cox 1991;Borenstein, Everett, & Feng 1996). It is specifically hardin densely populated domains where people may block therobots sensors for extended periods of time.

    RHINO employs a version of Markov localization, amethod that has been employed successfully by severalteams (Nourbakhsh, Powers, & Birchfield 1995; Simmons& Koenig 1995; Kaelbling, Cassandra, & Kurien 1996).RHINO utilizes a metric version of this approach (Burgardet al. 1996), in which poses are estimated in x-y- space.

    Markov localization maintains a probabilistic belief asto where the robot currently is. Let P (l) denote this be-lief, where l is a pose in x-y- space. Initially, when thepose of the robot is unknown, P (l) is initialized by a uni-form distribution. To update P (l), Markov location em-ploys two probabilistic models, a perceptual model and amotion model. The perceptual model, denoted P (s j l),denotes the probability that the robot observes s when itspose is l. The motion model, denoted P (l0 j l; a), denotesthe probability that the robots pose is l0 if it previously ex-ecuted action a at location l.

    Both models are used to update the robots belief P (l)

  • Fig. 4: Global localization. Obstacles areshown in black; probabilities P (l) in gray.

    Fig. 5: Typical laser scan in the presenceof people. The gray-shaded sensor beamscorrespond to people and are filtered out.

    Fig. 6: RHINOs path planner: Thegrey-scale indicates the proximity ofthe goal.

    when the robot senses, and when it moves, respectively.Suppose the robot just sensed s. Markov localization thenupdates P (l) according to Bayes rule:

    P (l j s) = P (s j l) P (l) (1)

    where is a normalizer that ensures that the resulting prob-abilities sum up to one. When the robot moves, Markovlocalization updates P (l) using the Theorem of total prob-ability:

    P (l0) =

    ZP (l0 j a; l) P (l) dl (2)

    Here a denotes an action command. These two updateequations form the basis of Markov localization. Strictlyspeaking, they are only applicable if the environment meetsa conditional independence assumption (Markov assump-tion), which specifies t