AUTONOMOUS BEHAVIOR-BASED EXPLORATION OF OFFICE

ENVIRONMENTS

Daniel Schmidt, Tobias Luksch, Jens Wettach and Karsten Berns

University of Kaiserslautern (TU)

Gottlieb-Daimler-Straße, 67663 Kaiserslautern, Germany

eywords:

Mobile robots, autonomous exploration, behaviour-based, indoor environments.

Abstract:

Besides safe motion control the gain of environmental knowledge is a key for a reliable home or ofﬁce service

robot. When being set into a completely unknown environment the robot has to be able to derive a certain

abstract internal representation of this world without any user interaction. This knowledge enables the robot

to know how to get from its actual place in one room to a target position in another room as a prerequisite

for transportation tasks for example. In this context, the combination of a behavior-based motion control

system and an abstract topological map based on geometric representations of rooms seems promising. As

the concept of motion and exploration behaviors facilitates to compete with noisy sensor information and

geometrically imprecise maps, it has been used to develop completely autonomous exploration strategies for

deriving topological representations of common indoor environments. The only prescribed world knowledge is

the fact that these environments are composed of rectangular entities (rooms) which are connected by openings

(doors). The developed system has successfully been tested in simulation and reality.

1 INTRODUCTION

Mobile service robots have to derive an internal

representation of their working environment au-

tonomously. When such a machine is installed in its

new habitat, it has to perform a certain initialization

phase for setting up its internal map before the user

can give any orders to it. Of course this map building

has to be executed without any user intervention.

One question is how abstract and accurate this rep-

resentation has to be to facilitate safe robot motion

and reliable task performance. In this paper a new

behavior-based control approach is presented that en-

ables a mobile robot to derive a topological map of

indoor environments completely autonomously. The

realized approach allows to compete with inaccurate

sensor distance information and imprecise mapping

results. The main challenge is the coordination of

the behavioral network in order to guide the robot

autonomously through priori unknown environments

only based on 2D distance information from laser

scanners and the knowledge of rooms being rectan-

gular.

To explain the proposed exploration system the pa-

per is organized as follows: section 2 summarizes pre-

vious work, section 3 introduces the sensor systems

of the applied mobile robot. Section 4 describes the

basics of the environmental representation approach

and section 5 introduces the applied behavior-based

control system. The exploration system is explained

in section 6, followed by test results in section 7. The

project achievements are summarized in section 8 and

supplemented by intended future enhancements.

2 RELATED WORK

Autonomous exploration mainly depends on sophis-

ticated exploration strategies. Common approaches

include heuristic methods which calculate the prob-

ability of new sensor informations at given positions

out of the actual or already retrieved sensor data.

One of such exploration methods is proposed by

(Surmann et al., 2003). It utilizes so-called next-

best-view-algorithms to calculate a position which

promises a maximum of new informations about the

environment. A new measurement is taken only at

this position. Another strategy is pointed out by (Sim

and Roy, 2005), who implemented an exploration

method that tries to minimize the error of the global

235

Schmidt D., Luksch T., Wettach J. and Berns K. (2006).

AUTONOMOUS BEHAVIOR-BASED EXPLORATION OF OFFICE ENVIRONMENTS.

In Proceedings of the Third International Conference on Informatics in Control, Automation and Robotics, pages 235-240

DOI: 10.5220/0001204002350240

 SciTePress

map by integrating the informations collected while

driving a trajectory.

Other strategies have been published before. (Ya-

mauchi et al., 1998) proposed a so-called frontier-

based exploration. Here evidence grids are used

for the detection of unexplored regions and for the

extraction of new exploration targets. A different

method attempts to close virtual chains around two-

dimensional features as (Wullschleger et al., 1999)

published. There an object or room is explored if all

sides have been detected by the robot.

These strategies do not match the mapping sys-

tem in this work (explained in section 4) so a new

behavior-based method has to be created.

3 MOBILE ROBOT MARVIN

The test bed for the proposed exploration strategy is

the autonomous indoor vehicle Marvin - its sensor

systems are shown in ﬁgure 1. Above the bumper

rail for safety aspects two SICK laser scanners are

mounted, one at the front (model S3000) and one at

the back end of the robot (model LMS200).

Figure 1: Front view and sensor system of mobile robot

Marvin.

Above the laser scanners, there is a belt of ul-

trasonic sensors, mounted below the hexagonal top

frame of the robot. The goal of this sensor belt is to

detect obstacles that do not intersect the measurement

plane of the laser scanners, as for example table tops,

which the robot cannot underpass due to its raised arm

(camera holder).

On top of the robot’s arm there is another LMS200

scanner for getting more reasonable measurement re-

sults in highly crowded areas. Soon it will be mounted

on a tilt unit for 3D measurements. Besides the laser

scanner, there are two microphones to detect the di-

rection of sources of noise. Finally, there is a standard

webcam mounted on a pan-tilt unit. It is used for the

detection of human faces, blobs of color and straight

edges in a scene.

4 ENVIRONMENTAL

REPRESENTATION

In this section the recognition and representation of

the environment based on 2D distance information

from the laser scanners is introduced as this is helpful

for understanding the exploration strategy. From the

sensor data an abstract rectangular room consisting of

four walls is generated and openings like doors are

registered as described in (Kleinl

utzum et al., 2005).

The dimensions of the created room comply with the

measured distance information. Figure 2 shows an

example of laser scanner data (a) and the generated

room (b) with an already passed door on the lower

side including the room local coordinate plane and

three additional openings. This example will be con-

tinued in chapter 6.1.

Figure 2: Robot Marvin surrounded by scanner data (a)

and the corresponding map representation (b) (top view).

Regarding this form of description some assump-

tions need to be mentioned:

• All walls inside the rooms are arranged right-

angled.

• Rooms can be described by using rectangles or

combinations of them.

• Walls are ﬁxed inside the environment and cannot

be moved.

Correspondingly the application is limited to rec-

tangular environments as they can be found inside of-

ﬁce buildings. Environments with other shapes will

be approximated by rectangles which may lead to in-

accurate maps of the rooms. These restrictions have

been made to keep the representation as abstract as

possible and to make it more robust against dynamic

changes or other interferences. A topology matrix

speciﬁes the connections between the respective room

maps for navigation tasks.

At the moment a room is deﬁned as well-known if

the robot has explored all walls by travelling closer

than two meters to each part of the walls. This aspect

will change in future when new objects are added to

the map and is reﬂected in the rating k(R

) ∈ [0, 1].

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236

5 BEHAVIOR-BASED ROBOT

CONTROL

The basic ideas of the behavior-based robot control

system is subject of this section. Since R. Brooks in-

vented his Subsumption Architecture (Brooks, 1986)

the behavior-based robot control is established as one

of the most powerful systems for controlling a com-

plex robot.

For the implementation of the exploration system

the behavior architecture introduced in e.g. (Albiez

et al., 2003) is used. Figure 3 shows the structure of

a single behavioral module. The input vector e repre-

sents sensor values or preprocessed information from

other modules. This input is transformed by the trans-

fer function F (e, ι, i) into the output u which repre-

sents motion control values or information for other

behaviors. The calculation is inﬂuenced by ι which

determines the activation of the module and by the in-

hibiting input i which counteracts the activation. The

outputs a and r represent the module’s activity respec-

tively the target rating (or satisfaction).

Figure 3: Structure of a behavior module.

When several behaviors take effect on the same

control values, their output can be fused using two

different strategies, realized by units denoted as fu-

sion nodes. Both nodes collect the controller outputs

u

and activities a

of the involved modules and com-

bine them to one control vector u. The maximum fu-

sion forwards the output vector of the behavior with

the highest activity to the fusion output. In contrast

the weighted fusion averages all controller values re-

garding the different activities.

Further information on the architecture can be

found in (Proetzsch et al., 2005).

Figure 4: Process of the global exploration.

6 GLOBAL AND LOCAL

EXPLORATION

This section describes the behavior based exploratory

strategy that enables the mobile robot Marvin to

gain knowledge about complex indoor environments

without any user intervention. The control sys-

tem has been integrated into the AutEx

system

(Autonomous Exploration of Indoor Environments)

as shown in ﬁgure 5. This structure contains percep-

tual components for mapping and localization as well

as a behavior-based steering system. It has been in-

troduced in (Kleinl

utzum et al., 2005) and updated to

its current state by (Schmidt, 2006).

Figure 5: Exploration structure of the robot Marvin.

Targeting a hierarchical structure that is easy to up-

grade a control system has been implemented with

different grades of abstraction. The lowest level is

composed of basic behaviors including safety aspects

and simple movements. Exploration behaviors and

additional components are implemented on higher

levels. The main exploration system consists of three

concurrent behaviors:

Local Exploration Collection of simple behaviors

which allow the exploration of the current room.

Path Tracker Drives the robot to the closest point of

interest by using a topological graph.

Global Observer Surveys the

Local Exploration

and

triggers the path tracking behavior, if for a period

of time no new informations have been retrieved.

Figure 4 shows a ﬂowchart of the exploration activ-

ity based on the interaction of these three components.

The idea is to separate the

Local Exploration

which

depends only on laser scanner data and the global ex-

ploration strategy that has access to the more complex

map data. By this means the robot can travel directly

based on sensor informations with a high reactivity.

AUTONOMOUS BEHAVIOR-BASED EXPLORATION OF OFFICE ENVIRONMENTS

237

The

Local Exploration

is mostly responsible for col-

lecting data about the environment. It is surveyed by

the

Global Observer

behavior which can guide the

robot by the

Path Tracker

module to an interesting

point.

6.1 Local Exploration

The ﬁrst part that has been implemented is the

Local

Exploration

. It has been realized as a set of different

simple behaviors that compete for the control of the

robot Marvin. From the outside view this group re-

acts like one behavior with the interfaces introduced

in section 5. All included behaviors only produce

turning commands, the forward driving is done au-

tomatically but can be inhibited. The structure of the

Local Exploration

can be seen in ﬁgure 6 - the mem-

bers are described in the following:

Random Cruise Simple behavior that drives the ro-

bot in a random direction. The activity is always

=1unless it is inhibited by any other active

local behavior - the target rating is set r

=0.

Hold Distance Left / Right Two behaviors which

try to keep a certain distance from obstacles on the

left and the right side equivalent to wall follow be-

haviors. The activity depends on the minimal dis-

tances d to the nearest obstacles and two thresholds

∈ R as seen in equation 1. The target rating

is computed by the needed movement of the robot

= |m|.

= min



max



1 −

d − d

− d

, 0



, 1



(1)

Pre Evasion Turns the robot away from frontal ob-

stacles or towards passages in front of it. Activity

and target rating r

are calculated as for the

distance holding behaviors.

Local Door Driving Allows the robot to leave the

current room by turning towards an opening inside

a wall even if this opening is not in front of the ro-

bot. The activity is set depending on the needed

movement m (equation 2), the target rating is set

LDD

=0.

LDD



1 |m| > 0

0 else

(2)

Narrow Driving This behavior is meant for a special

situation that only occurs that way on the real robot.

Inside narrow doorways several behaviors can be

active and counteract each other. Therefore this be-

havior suppresses all other local exploration mod-

ules so that the robot cannot turn and goes straight

ahead driven by the basic behaviours. The activity

is calculated in a similar way to the distance

holding behaviors, whereas the target rating is set

=0.

Figure 6: Interaction of the local exploration behaviors.

All these simple behaviors enable the robot

to explore a local area by following walls and

travelling through passages and narrow doors.

The activity of this group is determined by the

maximum of the internal activities a

LocExp

max(a

HDL

, ..., a

). The target rating

LocExp

=1− k(R

)

is calculated with the know-

ledge rating k(R

) ∈ [0, 1] for the current room R

As sensor input the local exploration behaviors use

an abstraction from the raw laser scanner data. There-

fore the space around the robot is split into several

sectors that reﬂect spatially discretized distance infor-

mation (denoted as

Robot Sectors

in the following) as

shown in ﬁgure 7. These illustrations correspond to

the scanner data and resulting map in ﬁgure 2. For

each sector several values are determined:

1. minimal distance of the laser scanner beams inside

the sector

2. maximal and minimal distances to the generated

walls of the current room

3. minimal distance to already passed doors

Depending on different states of the exploration the

local behaviors receive the minimum of 1. and 2. (ﬁg-

ure 7 (a)) to keep the robot inside the room, the min-

imum of 1. and 3. (ﬁgure 7 (b)) so that the robot can

leave the room only through an unknown opening or

only the scanner data as shown in ﬁgure 7 (c).

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238

Figure 7:

Robot Sectors

at different states of the exploration

inside the same room (corresponding to ﬁgure 2).

6.2 Global Exploration

In order to explore complex indoor environments

autonomously additional behaviors are needed that

guide the robot from room to room. This task is coor-

dinated by the

Global Observer

module based on the

Path Tracker

(see ﬁgure 4 for an overview).

The global exploration strategy works as follows:

The robot starts at an arbitrary position inside its en-

vironment by generating the ﬁrst room. Then the

Lo-

cal Exploration

becomes active and the robot starts

driving through the surrounding area by using the

laser scanner data until the room is well known. Then

the robot is allowed to leave the room by a local be-

havior that drives it through a narrow opening.

During the local exploration the

Global Observer

surveys the actions and becomes discontented, if no

new information is added to the map within a certain

period of time. So the activity is set a

=1if the

local exploration is active (equation 3).



1 a

LocExp

> 0

0 else

(3)

The target rating r

can be calculated by using

equation 4 with a counter C that increases if the map

data has not changed since the last check and is reset

otherwise. T

∈ N deﬁne how many cycles are

used for becoming unsatisﬁed with T

.Asa

result the local exploration can be longer active inside

poorly known rooms R

before the rating becomes

completely unsatisﬁed (r

=1).

= min



− (T

· k(R

))



(4)

At a maximum of dissatisfaction the

Global Ob-

server

triggers the

Path Tracker

. This behavior cre-

ates points of interest (short: POIs) from the map data

by searching for openings which haven’t been passed

through by the robot or for unknown parts of walls.

Furthermore it constructs a graph that allows the ro-

bot to travel to each point inside every known room.

The closest POI is selected by using the Dijkstra al-

gorithm. When the robot moves along this path the

Path Tracker

stays active, until the target position is

reached. To achieve this a variable t

determines if

the robot reached its target or not (equation 5).

=1− t

(5)

For surveying the robot’s path the

Path Tracker

be-

comes discontented if it cannot reach a point on the

path because of objects onto the point or a closed

doorway. Therefore a counter C

increases if the ro-

bot is closer than one meter to the next point and an-

other counter C

increases if it is moving away from

the next point. So the target rating (equation 6) is

calculated using these counters and two thresholds T

and T

= max





(6)

In the ﬁrst case the robot accepts the nearly reached

position and turns to the following way point. If the

other case occurs the

Path Tracker

has to change or

delete the actual edge inside the graph and recalculate

a new path from the actual position to the target point.

When reaching the destination the local behaviors

become active again and continue the exploration.

7 TEST RESULTS

The described exploration system has been tested in

simulation as well as on the robot Marvin. For the

simulation the scanner data is generated by using a

manually created map and adding noise to the values.

Figure 8 describes the values of the global behav-

iors during the exploration of a simulated ofﬁce envi-

ronment. The ﬁrst three rows are the activation ι

Loc

activity a

Loc

and target rating r

Loc

of the

Local Ex-

ploration

. Below the values of the

Global Observer

and of the

Path Tracker

module are shown.

It is obvious, that most of the time the

Local Ex-

ploration

is active and controls the robot. During

this simulation run the target rating of the

Local Ex-

ploration

Loc

is decreasing while the robot retrieves

more informations about the room. At about sec-

ond 60 the

Global Observer

becomes discontented be-

cause no new information is added to the map. As

the target rating r

reaches its peak the

Local Ex-

ploration

is turned off and the

Path Tracker

starts to

steer the robot to the closest point of interest. Near to

each waypoint the target rating r

increases as the

described counter C

is incremented. When the ro-

bot has reached the target position the

Path Tracker

loses its activation and the

Local Exploration

contin-

ues the exploration. This scheme is repeated more or

less frequently. It takes about 20 minutes to explore

the environment as shown in ﬁgure 9 with dimensions

of about 28 times 25 meters. The duration strongly

AUTONOMOUS BEHAVIOR-BASED EXPLORATION OF OFFICE ENVIRONMENTS

239

Loc

500 1000 1500 2000 2500 300

Figure 8: Activities of the applied global behaviors by the

time in

seconds.

depends on the amount of objects inside of the rooms

and the number of narrow passages that slow the robot

down.

In reality this exploration strategy could not be

tested as good as in simulation. The reason for that is

the limited possibility to detect the walls of rooms, if

the sight to them is disturbed by objects on the ﬂoor.

Therefore the real experiments have taken place in-

side an empty corridor similar to the one inside of

the simulation. For these exploration runs only the

local strategies excluding the door driving behavior

have been used because of the limitations which are

linked to mapping problems. Separately the other be-

haviors like

Path Tracker

or the

Local Door Driving

have been tested as well and promise good results for

global exploration in reality.

Figure 9: Autonomously explored simulated environment

with navigation graph.

8 CONCLUSION

This paper introduced a hierarchical behavior-based

exploration system. Three main components are

shown and validated in simulated and real experi-

ments: The

Local Exploration

to explore the current

region, a

Path Tracker

behavior for navigation tasks

and a

Global Observer

to fulﬁll a global strategy.

Future Work will mainly consist of an enhance-

ment of the room recognition methods and the inte-

gration of more sensors. Furthermore the mapping

itself could be upgraded by integrating new map ob-

jects. At last additional exploration behaviors will fol-

low that utilize sensors like microphones, cameras or

ultrasonic sensors.

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