Behaviour Competition
Philipp Althaus and Henrik I. Christensen
Centre for Autonomous Systems, Numeri al Analysis and Computer S ien e,
Royal Instituteof Te hnology (KTH), S-10044 Sto kholm, Sweden
Abstra t
Navigation in large-s ale environments is omposed of di erent lo al tasks. To
a hieve smooth swit hing between these tasks and thus a ontinuous ontrol sig-
nal, usually a pre ise map of the environment and an exa t pose estimate of the
robotareneeded.Botharehardto ful llforexperimentsinreal-worldsettings.We
presentasystemthatshows howone anrelaxtheneedfora uratemetri models
of the environment while at the same time a hieving smooth task swit hing. To
fa ilitate this s heme the dynami al systems approa h is used,whi h in orporates
behaviour oordination through ompetition ina dynami framework.Feature de-
te torsusesonar datatoprovidemeansforlo alnavigation.Thisability ombined
with a simpletopologi al map onstitutes a omplete navigationsystem for large-
s aleoÆ eenvironments.ExperimentsshowedthataS outrobotusingthiss heme
isableto su essfullynavigatethroughourwholeinstitute.Throughtheuseofthe
dynami behaviour oordination, swit hing between the navigational tasks o urs
ina smoothmanner leadingto ontinuous ontrolofthe platform.
Key words: behaviour oordination,mobilerobots, robotnavigation, dynami al
systems,hybriddeliberative systems
1 Introdu tion
A omplete navigation system of an autonomous robot is traditionally of a
hybridnature.It onsistsofa rea tivepart, whi h allowstodeal with unfore-
seenlo alevents.Then,thereisadeliberativepart, whi htakes are ofglobal
issues like path planning. See [3℄ for an overview of di erent ar hite tures.
Email addresses: philippnada.kth.se(PhilippAlthaus),hi nada.kth.se
(HenrikI. Christensen).
s alestru tures. Fortheimplementationofthis mapand itsusethere aretwo
on eptually di erent approa hes, using either an exa t geometri map or a
qualitativemap (e.g. topologi al map).
An exa t geometri map (forexample, a grid map[11℄ ora feature map [10℄)
allowstodetermineanappropriate ontrola tionatanyrobotposition,whi h
leadstosmooth ontrolandtraje tories.However,thispre isemap rsthasto
beobtained and the exa t robotposition has tobe known atalltimes. These
onditions are hard to ful ll and might be omputationally very expensive.
In ontrast, the use of a topologi al map requires only an approximate guess
of the robot pose; also the small-s alegeometry does not have to be known
pre isely. Through sensory informationfrom the robotit is determined when
therobothasrea hedanodeinthemap.Thisinformationisenoughtoswit h
between the ne essary behaviours to deal with the navigation task at hand.
The Polly system [5℄, for example, shows how su essful navigation an be
a hieved using thissimpletype ofqualitativemap.However, through dis rete
swit hing between tasks the ontrolsignals are not ontinuous. Furthermore,
alsothe traje toryof the robot is not smooth anymore.
In this paper we propose a uni ed ontrols heme ombining the advantages
ofea hof thetwoapproa hes above.Wepresentanavigationsystem foroÆ e
environments using feature dete tors whi hextra t representations fromsen-
sory data. This geometri pla e re ognition fa ilitates safe lo al navigation.
To apture the large-s ale properties of the environment a simple topologi-
almap is used. Nevertheless, through smooth task swit hing ina dynami al
system framework, ontinuity in the ontrol signal an be a hieved. The dy-
nami alsystemsapproa htobehaviourbasedroboti s introdu edby S honer
and Dose [15℄ provides this ne essary framework. It utilizes a ompetition
s heme among individual behaviours. So far, this ompetition has only been
exploitedinsimulationwork[15,16,9℄usingsimpli edsmall-s alesettings.The
system proposed in this paper, however, a ts in a large-s ale real-world envi-
ronment usinga topologi almap ontainingthe global informationne essary
for navigation. This information about the whereabouts of the robot is still
of a dis rete nature; nevertheless, it is only used as parameters in the dy-
nami oordinationamongthebehaviours. Thisdynami system thenensures
smooth swit hing between tasks. In this way the dis rete nature of a quali-
tative map is ombined with the need for ontinuous navigation in a uni ed
ontrols heme. Inessen e, the systempresented shows thatone an relaxthe
needfor ontinuousposeestimationwhilemaintainingapossibilityforsmooth
behaviourswit hing.
Initially, the topologi al map fa ilitating task swit hing is introdu ed inSe -
tion 2. Then, in Se tion 3 the geometri representations used for lo al navi-
gation are presented. The dynami al systems approa h is outlinedin Se tion
periment is presented in Se tion 5, while a summary and avenues for future
resear h are outlinedin Se tion6.
2 The Topologi al Map and its Use
The topologi almap is used by the system to swit h between di erent tasks,
in essen e to a tivate an appropriate set of behaviours. Hen e, it ontains
qualitative information about the large-s ale stru ture of the environment.
This information is re e ted in nodes and edges that onne t these nodes.
The nodes stand for important pla es in the environment. There has to be
one in front of ea h door, at ea h orridor rossing and at other pla es of
interest(e.g.goallo ationsand hargingstation).Ea hnode hasalo ationin
a xed oordinatesystem.The edgesthat onne t thesenodes anbeof three
di erenttypes: room, orridor,door.Duetothelimitedamountandsimpli ity
of information in the topologi almap, it is a matter of minutes to onstru t
a new one for a previously unknown oÆ e environment. Figure 1 shows the
topologi almapofour institute. Toplanapath between twoarbitrarynodes,
thesystem ondu ts abreath rst sear hthrough thegraph.This isafeasible
solutionfor environments of the size of anoÆ e area.
Fig. 1. The topologi al map of our institute.The ir lesin the map depi t nodes,
whi h have an exa t lo ation in a oordinate system. Edges are of three di erent
types: orridor (thi k line), room (dashed line), and door (thin line). Additional
nodes for goal points and starting positions an be added arbitrarily. The nodes
denoted with \Charging Station" and \Goal Point" orrespond to the initial and
nal lo ation of the experiment des ribed inSe tion 5. The nodes in gray are the
onesused to exe utethisplan.
At plan exe ution, it is assumed that the initial position and orientation of
the robot are known (e.g. harging station). From there odometry is used
to determine the robots lo ation.This introdu es errors in the estimation of
the exa t position of the robot, but is totally suÆ ient to determine if the
system is in the vi inity of a node. This information is enough to evoke the
s ale stru tures of the environment (see Se tion 4.2). Note that the error in
the position estimate would grow bigger than desired on long trials over a
great distan e. To avoidthis de ien y, the pose estimate is orre ted based
ondete ted features(see Se tion3).
3 Extra ting Geometri Representations from Raw Sensor Data
Inour experimentsweused theS outrobotfromNomadi Te hnologies(Fig-
ure 2). The platform has a ylindri al shape with a diameter of 38 m and
moves at a speed of up to 1 m/s. The robot is equipped with a ring of 16
evenly spa ed ultrasoni sensors. The per eption and geometri re onstru -
tion of obsta les, walls and doors isbased solely onthe informationprovided
by these sensors. Ea h sonar has a beam width of 25 Æ
and a dete tion range
of 6 to 255 in hes. The robot possesses a two wheel di erentialdrive lo ated
atthegeometri enter whi hallows omni-dire tionalsteeringatzeroturning
radius.The wheelsare equipped with en oders to obtain odometri data.
Fig.2.The S out robot usedintheexperiments.
The ontrol system onsistsof ve di erent behaviours: orridor follow-
ing,wall avoidan e,door passing, obsta le avoidan e,and go to.
Thesebehavioursensuresafenavigationgivenanindividualtask.Hen e,they
allneed some geometri representation of the lo alproperties of the environ-
ment.
To navigate along a orridor two behaviours have been designed: orridor
following and wall avoidan e. They are based on the orientation of
the orridor and the distan e to itswalls. To obtainthis informationthe 200
most re ent sonar readings are kept in a FIFO bu er. A Hough transform
[4℄ is invoked on the sonar data every few se onds in order to extra t the
pair of parallel lines (one on either side of the robot) that oin ide with the
largest number of sonar e hos. No assumptions on the width or dire tion of
the orridor are made.
large enough opening ina wall.In order to nd a door, when the robot nds
itself in a orridor, the dire tion to the dete ted orridor wall is used. The
25 most re ent sonar readings, that lie in the dire tion of this wall and not
more than 50 m behind it, are kept in a FIFO bu er. The largest angular
segment (from the robots point of view) that does not ontain any sonar
reading is determined.If this segment is greaterthan 15 Æ
we onsider a door
to be dete ted and its dire tion is de ned as the entre of the free segment.
Thispro essisinvoked atevery ontrol y leof therobot.Notethatthisdoor
dete tor is very rude, due to the simpli ity of the sensors used. Espe ially
half blo ked doors, with passages that are to small to pass, will sometimes
still be dete ted as doors. However, situations like this are resolved by the
oordination amonga doorpassing and anobsta le avoidan e behaviour(see
the experimental results in Se tion 5).Further, if the robot is in a roomthe
samestrategy todete t adoorisapplied.However, rstthe wallatwhi hthe
door islo ated has to be extra ted. In order to do this a Hough transform is
invoked onthe 100 most re ent sonar e hos.
Duetothelimitedangularresolutionofsonarsensors,thegeometri represen-
tation of obsta les (used by the behaviour obsta le avoidan e) is rather
simple and losely linked to the a tual per eption of the robot. Out of the
50 most re ent sonar readingsthat donot belong to dete ted walls, the ones
in the frontal half plane of the urrent robot heading are onsidered. Obsta-
les are re onstru ted from these dete ted e hos in as ending order of their
distan e to the robot.The e ho losest to the robotde nes the rst obsta le
whose orientation in the robot frame is given by the axis of the sensor that
re eivedthe e ho.A newobsta leisre orded foreverysubsequente howhose
orientationdi ers by anangle of atleast 22.5 Æ
from any previously identi ed
obsta le. New obsta les are added in an in remental fashion until the sonar
bu er ontains no further e hos. Obsta le re onstru tion is invoked at every
ontrol y le of the robot. Noti e, that our representation only onsiders the
distan etoanobsta lebut ignoresitsshapeorsize. Despiteitssimpli ity,the
hosen representation ispowerfulenoughto su essfullynavigate in luttered
areas [2,1℄.
Ea h of the above dete tors keeps a ertainnumberof the most re ent sonar
readingsinaFIFObu er.While olle tingthesereadingstherobotisdriving
ashortdistan e.Odometryisused,to al ulatethe lo ationofsonarreadings
taken at di erent robot positions, whi h introdu es further un ertainty in
the sonar data. These errors, however, are omparatively small and hardly
in uen e the performan e of the behaviours.
The behaviour go to aligns the robotsheading with the dire tion of a goal
point. We do not use any dete tor for this goal point yet; its lo ation is only
de ned by anode inthe topologi al map (Se tion 2).
todeterminewhi hoftheabovedete torsshouldbeinvoked.Theinformation
about the exa t lo ation of its nodes and the robots position estimate (from
odometry)determineifthe robot ndsitselfina orridororinaroomand/or
lose to a door. The informationfrom the dete ted features, in turn, is used
to update the position estimate. Otherwise, the error in this estimate may
growbiggerthandesiredusingonlyodometry.Hen e,ina orridortherobots
omputed orientation and position are adjusted relativeto the orridor walls
ea htimetheHoughtransformisinvoked(i.e.everyfewse onds).Inaddition,
every timeadoorispassedorientationandpositionrelativetothe doorposts
an be updated a ordingly.
4 Behaviour Coordination
The dynami alsystems approa h has been used to design the individual be-
haviours and their intera tion. The on eptualframework of this approa h is
based on the theory of nonlinear dynami al systems [13℄. In Se tion 4.1 we
only providea brief outline of this framework and referthe interested reader
to [16℄ for a more detailed des ription. Se tion 4.2 des ribes the design of
our system,espe iallythedynami oordinationof theindividualbehaviours.
Note that both, the behaviours and their oordination, are implemented in
a uni ed framework to provide a ontinuous ontrol signal at all times. The
parametersof thisframeworkare an hored inthetopologi almap(Se tion 2)
and in the features extra ted from the sensory data (Se tion 3).
4.1 Dynami alSystems Approa h
A behaviour b emerges from the time evolution of the behavioural variables
des ribed by the ve tor ~x . In a navigation task for example the robot head-
ingand velo ity onstitute the set of behavioural variables.In the dynami al
system des ribed by
_
~x=
~
f
b
(~x) (1)
the fun tion
~
f
b
an be interpreted as a for e a ting on the behavioural vari-
ables.Thisfor eisdesignedsu hthat thedesiredvalues of~x(e.g.dire tionof
atarget)formanattra torandundesiredvalues(e.g.dire tionofanobsta le)
forma repellor in the dynami s of the behavioural variables. The fun tion
~
f
b
dependsontherelativeposebetween therobotanditsenvironment.However,
the dynami s of ~x takes pla e on a mu h faster time s ale than the gradual
hanges that emerge in
~
f
b
as a result of the robot's motion. This property
assures that the dynami variables remain lose to the attra tor state at all
vidual ontributions
~
f
b :
_
~ x=
X
b jw
b j
~
f
b
(~x)+noise (2)
The weights w
b
2[ 1;1℄ de ne the strength of ea h behaviour and are om-
puted based on the per eived ontext of operation. The noise has a small
amplitude and merely ensures that the dynami s es apes unstable x-points
(repellors).Coordinationamong behaviours ismodelledbymeans of anaddi-
tional ompetitivedynami sthat ontrols the weightsw
b
,whi hevolveinthe
following fashion:
b _ w
b
=
b (w
b w
3
b )
X
b 0
6=b
b 0
;b w
2
b 0
w
b
+noise (3)
The rst term onstitutes a pit hfork bifur ation,i.e. the dynami spossesses
stable x-pointsat
w
b
= 8
<
:
1 if
b
>0
0 if
b
<0
(4)
The fa tors
b
2 [ 1;1℄ are alled ompetitive advantages. They determine
the degree to whi h a behaviour is appropriate and desirable in the present
ontext. The se ond term in equation 3 aptures the ompetitive dynami s
in that an a tive behaviour b 0
of higher priority suppresses the a tivation of
another on i tingbehaviourb.Hen e, thefa tors
b 0
;b
2[0;1℄are alled om-
petitive intera tions. For jw
b 0
j 1 and
b 0
;b
>
b
, the point w
b
= 0 be omes
the new stable x-pointof behaviour b, despitea positive ompetitiveadvan-
tage
b
>0.A detailedanalysisof howthestabilityof x-pointsvaries a ross
di erentvaluesof ompetitiveadvantagesandintera tionsisgiven in[9℄.The
time onstant
b
determines the rate atwhi h the behaviours are swit hed on
and o .Similartothe behaviouraldynami s,the noise termhelpsthe system
toes ape unstable x-points interms of behaviour oordination.
4.2 System Design
We hose the robot heading as the behavioural variable of the dynami-
al system as it o ers the advantage that the behaviours an be naturally
expressed in this variable. Furthermore, the ommanded turn rate _
an be
dire tly appliedas a ontrola tion tothe robot. The translationalvelo ity is
regulatedby anexternal ontrolloop,whi hredu es therobotspeedbasedon
two values: 1) the proximity of nearby obsta les, for safety reasons, 2) ahigh
turn rate _
, to assure that the robots heading remains lose to an attra tor
state atall times (see Se tion4.1).
ridor following, wall avoidan e, door passing, obsta le avoid-
an e, and go to. The design of these behaviours and their fun tional form
f
b
( ) (equation 1) are motivated and dis ussed in our previous work [2,1℄.
These behaviours or ombinations of them are able to deal su essfully with
theenvironmentonthesmalls ale.In[2℄,forexample,weshowed analyti ally
that the system an reliably distinguish between those passages that are too
narrow topass and gaps that are wide enough totraverse safely.
Theoverall dynami softhe system isobtainedfromthe weighted summation
of individualbehaviours based onequation 2:
_
= X
b jw
b jf
b
( )+noise (5)
with b 2 fgoto;obst; orr;wall;doorg. Forthe oordination of the behaviours
the ompetitiveadvantages
b
,the ompetitiveintera tions
b 0
;b
,andthe time
onstants
b
in equation3 have tobe hosen appropriately.
The ompetitive advantages re e t the relevan e and appli ability of a be-
haviourin a parti ular ontext. Obviously, go toshould bea tivated when-
ever the agent nds itself in a room and is supposed to approa h a goal;
otherwise, itis turnedo . Ifthe robotisin a ertain room, is determinedus-
ing the estimate of the robot's positionand the topologi al map (Se tion 2).
For
goto
2(0;1℄the behaviour go to isswit hed on. To have the possibility
for any ompetitive intera tion
b;goto
2 [0;1℄ to be greater or smaller than
goto
, a value of 0.5is hosen for the ompetitiveadvantage. Hen e:
goto
= 8
<
:
0:5 if ina room
0:5 otherwise
(6)
Equivalently, orridor following and wall avoidan e are relevant if
the robotis in a orridor.
orr
=
wal l
= 8
<
:
0:5 if in orridor
0:5 otherwise
(7)
The ompetitive advantage of door passing is tightly oupled to the per-
eption. It is set to a positive value as soon as the door we want to pass is
dete ted (se tion 3).
door
= 8
<
:
0:5 if door dete ted
0:5 otherwise
(8)
Therelevan eofobsta leavoidan edependsonthenumberandproximity
of the obsta les urrently surrounding the robot.As ameasure re e ting this
= X
i e
d
i
(9)
The sum goes over all obsta les i and the d
i
stand for the distan e to them
as a multiple of the robot's radius. The ompetitive advantage of obsta le
avoidan e is,then, omputed a ording to
obst
=tanh(
0
) (10)
The onstant
0
determines the density above whi h obsta le avoidan e be-
omes relevant (i.e.
obst
>0).The tangent hyperboli ensures that the mag-
nitude of
obst
is limitedto the interval [ 1;1℄.
The ompetitive intera tion
b 0
;b
re e ts the degree to whi h an a tive be-
haviourb 0
suppresses another behaviourb. In fa t,there are situations where
behaviours would interfere with ea h otherin anundesirable, ounterprodu -
tivemanner.Adoorthatishalf-blo ked byanobsta lemightstillbedete ted
as a door, although the gap to pass is a tually too narrow. Hen e, we want
obsta le avoidan e to suppress door passing in the presen e of a high
obsta ledensity. Furthermore,if twoobsta les lie lose toea hother, the dy-
nami s of generates a weak repellor in the middle of them (this has been
shown in [2℄). This repellor, however, ould be dominated by an attra tor
of another behaviour, whi h would inevitably lead to ollision.Consequently,
obsta le avoidan e ought to suppress go to and orridor follow-
ing as well, if the obsta le density (equation 9) ex eeds a riti al threshold
. This prioritization is a hieved by appropriately hoosing the ompetitive
intera tions:
obst;goto
=
obst; orr
=
obst;door
= 1
2
(1+tanh(
)) (11)
The onstant
determinesthedensityatwhi hobsta leavoidan esuppresses
the otherbehaviours (
obst;b
>0:5).The fun tionalformof theterm is hosen
su h that
obst;b
2 [0;1℄. Sin e there exist no potential on i ts among any
other pair of behaviours, all other ompetitive intera tions
b 0
;b
are set to
zero.
The time onstants
b
determine the time s ale at whi h the behaviours are
swit hed onand o respe tively.
obst
is hosenverysmall,su hthattherobot
rea ts almost immediatelyif anew obsta leis per eived. The same holds for
wal l
. As soon as a door is dete ted, the robot should turn towards it before
driving out of dete tion range again. Consequently,
door
is also hosen to be
small. The dynami sof w
goto
and w
orr
evolveat aslowerrate
goto
=
orr
obst
. On e obsta le avoidan e be omes less relevant, e.g. after the robot
ir umnavigates anobsta le, the other behaviours swit hongradually rather
than ausingjitter amongthemselvesand obsta le avoidan e.
In the following we dis uss a path followed by the robot.The result is quite
representativeforresultsobtainedfromotherexperiments.Figure3showsthe
traje toryoftherobotduringatypi altask:drivingfromthe hargingstation
in the living room to a goal point in the manipulator lab. During this task,
the robot overed adistan e ofabout 50meters.Inthe middleof the orridor
the way was blo ked by people. Therefore, the robot was ir ling for about
a minute before the passage was leared and it was able to pro eed (see [2℄
fordetailsonbehaviourswit hinginblo ked orridors). Throughthis ir ling
theerror inthe positionestimate,obtained fromodometryonly,would betoo
large.However,by orre tingthisestimateonthebasisofthedete ted orridor
(Se tion 3) the error was kept small and the robot ontinued su essfully to
the goalpoint.
0 10m
living room manipulator lab
Fig.3.Thetraje toryfromatrialinourinstitute.Therobotstartedatthe harging
stationinthelivingroomand pro eededthroughthe orridorto agoalpointinthe
manipulatorlab.The boldre tangle an beseen enlargedinFigure 4.
Figure4visualizes moredetailson thetraje toryfromfollowinga orridorto
passing a door and rea hing a goal point. In addition, the absolute values of
the di erent weightsjw
b
j(equation5)are plottedover time.The labelledti s
on the time axis orrespond to the situations A-F that are des ribed below.
Noti e,thatthetimedi eren ebetweentwosu essiveti sisnotproportional
to the path length between the orresponding events, as the robot does not
moveat a onstant speed.
A) The vi inityof the next node in thetopologi almap was rea hed and the
doorwasdete ted. Door passing wasgraduallyswit hed onandguidedthe
robot towards the door. Corridor following was turned o on a slower
times alethanwall avoidan e.B)Thedoorwasblo ked byapersonleav-
ingtheroom.However,obsta le avoidan e ompetedwithdoorpassing
and nallydea tivatedthelatterduetoahigh obsta ledensity (equation11).
Consequently, the robot turned away from the door. C) The door was de-
te ted again and obsta le density has de reased. Therefore door passing
wasgraduallya tivatedandthe robotturnedtowardsthe dooragain.D)The
robot passed the door. Due to the high obsta le density door passing was
swit hed o again and obsta le avoidan e guided the robot through the
opening. E) The vi inity of the next node was rea hed. Thus, go to was
B
C A F
D E
A B C D E F
0 1
time
b
A B C D E F
0 1
time
b
Fig. 4. Details on the task swit hing from following a orridor to rea hing a goal
point in a room. The left image shows an outline of the traje tory of the robot.
Thegrayellipsedenotesapersonthatwasleavingtheroom,whentherobotwasat
lo ationB.Ontherighthandside,theabsolutevaluesoftheweights(seeequation5)
are plotted over time: jw
orr
j(upperplot, solid urve), jw
wal l
j (upper plot,dotted
urve), jw
door
j (upper plot, dashed urve), jw
obst
j (lower plot, dotted urve), and
jw
goto
j (lower plot, solid urve). The situations A-F in the left image orrespond
to the time instan es labeled in the plots. These situations are explained in more
detailin thetext.
graduallyturnedonleadingtoasmoothswit hintasksagain.F)Eventually,
the goalpoint was rea hed and the trialwas ompleted.
Note that in Figure 4 not the a tual traje tory of the robot is plotted, but
thepositionestimateobtained fromtheodometry.Thesharp bendrightafter
passing the door (D) is therefore not a turn that the robot a tually made. It
is simply an artifa t of updating the position estimate after having passed a
door (see Se tion 3). Further, if the door was losed or the doorway had not
been learedaftera ertainamountoftime,theplanningmodulewoulddete t
this. It would sear h for an alternative path through the topologi al map. If
thereis nootherway torea hthe goal,the robotrepeats stepsC andDuntil
it eventuallywill beable topass.
In se tion4.2 the parameter values of the behaviour oordination s heme are
motivatedandexplained.However, animportantquestionisthe sensitivityof
thesystemtodi erentparametervalues.Ourexperien eshowsthatthepre ise
values of the ompetitive advantages,
b
, (equations 6, 7, 8, 10) are not that
important as long as the signs are orre t. However, the performan e of the
system isquitesensitive tothe ompetitiveintera tions,
obst;b
,(equation11).
This means that the riti al obsta le density,
, has to be hosen arefully,
su hthatobsta le avoidan esuppressesthe otherbehaviours atthe right
moment.
Feature dete tors were used to extra t environmental representations from
the sensory data obtained by the sonars of the S out robot; namely orri-
dors, doors, walls, and obsta les. This geometri information fa ilitates the
single behaviours to perform individual tasks: navigating safely along orri-
dors, through doors and around obsta les. To determine whi h one of these
tasksisappropriateina given ontext, asimpletopologi almapand odomet-
ri data from the robot were used. To ombine the ontinuous ontrol of the
di erent behaviours with the dis rete information from the qualitative map
we deployed the dynami alsystems approa h tobehaviourbased roboti s. In
this way smooth swit hing between the navigational tasks ould be a hieved.
In essen e, this omprises a uni ed ontrol s heme that relaxes the need for
a urate metri models of large-s ale environments, through embedded pla e
re ognition inthe behaviours and behavioural ompetition.
This framework has been su essfully tested in the premises of our institute
(70 20 meters). An example of an experiment has been presented, whi h
shows that the system is apable of ondu ting long trials through a large-
s ale real-world oÆ e environment. It an also be seen that the ompetition
among the behaviours is able to deal with more omplex situations like half-
blo ked doors.
There are other approa hes to navigate in large-s ale environments. Many
of them (Xavier [7℄, for example) need more detailedmodels of the environ-
ment and sophisti ated algorithms (e.g. Markov de ision pro ess models) to
determine an appropriate ontrol a tion for the robot at all times. An other
approa h [14℄ uses, as we do, the superposition of di erent lo al behaviours
(motor s hemas). However, dis rete ontext hanges lead to dis rete hanges
in ontrol. The same holds for a variety of systems using topologi al maps
(Dervish[12℄,for example).Inour system thisinformationfromthe topologi-
almap anbemergedwiththe ontinuousnatureoftheindividualbehaviours
using thedynami al system approa h. Thisleads toa uni ed ontrols heme.
At rstsight,itseemsthatthe oordinationframeworkwouldnots alewellto
alargenumberofbehaviours,sin ethereisaparameter,
b 0
;b
,tobedetermined
forea hpairofbehaviours.However,inourexperien e,thisshouldnotbe ome
aproblem.Asinoursystem,thereare usuallyfewbehaviours on i ting with
ea hother. This means that most ompetitiveintera tions are set tozero.
Theuseofsonarsasthetheonlysensorsrestri tsoursystemindi erentways.
The representations of the environment are rather simple, whi h an lead to
problems (e.g. if two doors are right next to ea h other). Future resear h
in this proje t will be dire ted towards integration of more a urate sensors
more sophisti ated sensing apabilities, we willalso try to solve the problem
of globallo alization(negle ted in this paper) using justa simpletopologi al
map.In addition,advan ed sensing apabilitieswould alsoprovideabasis for
learningthetopologi almapbythesystemitself.This ouldbea hieved using
moresophisti atedmodulesfor pla ere ognitionasshown in[8℄.Further,the
generality of the behaviour oordination s heme has to be exploited. Up to
now, it has only been used for navigation behaviours in fet h-and- arry type
tasks. It isplanned toapply the framework toa di erent platform[6℄, where
the emphasis lies onhuman-robot intera tion.
A knowledgment
This resear h has been sponsored by the Swedish Foundation for Strategi
Resear h. The support is gratefully a knowledged.
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Philipp Althaus is a PhD student at the Centre for Autonomous Systems at
the Royal Institute of Te hnology (KTH) in Sto kholm, Sweden. He gradu-
ated1998inphysi sfromtheFederalInstituteofTe hnology(ETH)inZuri h,
Switzerland. Until 1999, he worked as a resear h assistant at the Institute of
Neuroinformati s at the University of Zuri h. His resear h interests in lude
behaviourbased roboti s, navigation, and mobilerobots ingeneral.
Henrik I. Christensen is Professor of Computer S ien e at the Royal Insti-
tute of Te hnology and dire tor of the Centre for Autonomous Systems. He
was awarded M.S . and Ph.D. degrees in 1987 and 1989, respe tively, from
Aalborg University, Denmark. His resear h is primarily in dynami vision,
robot navigation and systems integration. He has published/edited 7 books,
6spe ial issues and 140 papers. Dr.Christensen isthe oordinator of the Eu-
ropean Roboti s Network, EURON, and a resear h oordinator for the EU
network on Cognitive Vision. In addition he is on the editioral board of the
journals IEEE Transa tions on Pattern Analysis and Ma hine Intelligen e,
International Journal onRoboti s Resear h, International Journalof Pattern
Re ognitionand Arti ialIntelligen e, Ma hine Visionand Appli ations,and
Roboti s and Autonomous Systems. He maintains a tive ollaborations with
resear h groupson three ontinents.
