MAP EXPLORATION USING A LINE-BASED FORMATION OF

MOBILE ROBOTS

Bart Wyns, Jens Boeykens and Luc Boullart

Dept. of Electrical Energy, Systems and Automation, Ghent University, Technologiepark 913, Zwijnaarde, Belgium

Keywords:

Map exploration, Formation strategies, Mobile robots, Concave obstacles.

Abstract:

Exploration of an unknown environment is a well-studied problem for single robot systems. However, using

just a single robot limits the speed in which a map can be fully explored. Using a multi-robot approach,

a noticeable performance gain can be achieved. In this article a line-based formation strategy to explore a

static area is introduced, without making any assumptions about the shape of the obstacles within. A software

simulator including this line-based formation strategy was built to evaluate performance in different environ-

ments. Results show that the robot formation can easily get around both convex and concave obstacles whilst

constructing a map that is both complete and correct.

1 INTRODUCTION

Map exploration is one of the most widespread tasks

performed by mobile robots, due to many practical

applications. Mine ﬁeld clearance, planetary explo-

ration and rescue missions are just three out of many

examples. It is therefore considered as one of the fun-

damental problems in mobile robotics. Since the po-

tential of a single robot is quite restricted, an increas-

ing amount of research is focused on multi-robot ex-

ploration (A. U. Irturk, 2006; K. Easton and J. Bur-

dick, 2005; W. Burgard et al., 2005; W. Kerr et al.,

2005; S. Weihua et al., 2006; Simmons et al., 2000).

An increased performance, distributed sensing and

higher fault tolerance are evident motives. However,

moving from one robot towards a multi-robot setup

also strongly increases complexity.

Exploring unknown territory can be categorized in

complete coverage (I. Rekleitis et al., 2004) and no

complete coverage (as in (Rogge and Aeyels, 2007)

for example). The latter use a zigzag formation with

two leader robots. Whenever an obstacle is found, the

formation beaks up in two groups, always between the

middle robots. The major drawback of their strategy

is the lack of support for concave obstacles. Further-

more recursive splitting is bounded by the number of

available robots, which in turn limits the density of

the obstacles spread across the area. Coordinating the

robots with a formation obviously implies establish-

ing and maintaining that formation. In (J. Fredslund

and M. J. Mataric, 2002) this is achieved with local

sensing and minimal communication. Multiple robots

almost always need to communicate in order to avoid

collisions and minimize repeat coverage. It is often

assumed that communication is limited (W. Burgard

et al., 2005), sometimes with the restriction of line-

of-sight (I. Rekleitis et al., 2004; J. Ota, 2006). In this

article we shall not adopt this assumption, considering

it as future work. If communication is indeed limited,

a message can always be passed on from one robot

towards another until its destination is reached, just

as if the robots were forming a chain. A more com-

plete and general overview of exploration and cover-

age strategies with multiple robots is found in (A. U.

Irturk, 2006; H. Choset, 2001).

The main goal of this article is to design a strategy

which guarantees correct exploration with multiple

robots, resulting in a complete map of the area. We

only require free space to be covered by the robot’s

sensors, not by the robot itself. A formation is used to

coordinate the robots which explore a room one slice

at a time. Furthermore we don’t make any assump-

tions about the shape of the obstacles in the environ-

ment, unlike many related work. Both concave and

241

Wyns B., Boeykens J. and Boullart L. (2010).

MAP EXPLORATION USING A LINE-BASED FORMATION OF MOBILE ROBOTS.

In Proceedings of the 2nd International Conference on Agents and Artiﬁcial Intelligence - Agents, pages 241-244

DOI: 10.5220/0002591002410244

 SciTePress

convex obstacles are thus allowed. At the same time

we try to obtain maximum beneﬁt from the number of

robots that are available for exploration.

The remainder of this paper is organized as fol-

lows. Firstly, we describe the exploration strategy for

a general type of robot using a theoretical approach

in section 2. More details about the implementation

within the simulator can be found in section 3. Fi-

nally, we discuss the results in section 4, followed by

some conclusions in section 5.

2 ALGORITHM DESCRIPTION

In our approach, robots are ordered on a straight line.

An important advantage of a straight line is its width,

which is the largest of all possible formations. In this

way a large part of the map can be explored at once. In

addition it’s a very simple formation, which allows for

easy establishment via a chain of friendships (J. Fred-

slund and M. J. Mataric, 2002). As for the exploration

itself we use a complete sensor coverage approach as

in (Rogge and Aeyels, 2007).

Like many previous work, the robots explore the

environment slice per slice. The deliberative ap-

proach differs a lot from traditional algorithms (such

as behavior-based systems) as we don’t simply split

the robot team in two groups, each wall-following a

different side of the obstacles. Instead, two separate

phases are executed when obstacles are found. If one

robots detects an obstacle, this is communicated to

the team and all robots stop. Next, they start the ﬁrst

phase in which all new obstacles are added to the map

(fully explored via wall-following). This is called the

exploration phase and is illustrated in ﬁgure 1. When

no new obstacles can be found this phase is termi-

nated and the robots initiate the second phase. In this

phase the robots move round the obstacles until each

robot has passed it and the formation is regrouped. If

during the second phase a new obstacle is found or an

obstacle is preventing the formation to regroup, the

robots go back to the exploration phase. This process

continues until the formation is reestablished and no

obstacles are blocking their path. The formation can

then explore the remainder of the slice.

Once no new obstacles can be found near the

robots, they start moving round each obstacle block-

ing their path. Since each of those obstacles is already

added to the map, they can locate their correct goal

position. Each goal position is chosen so that all ar-

eas are explored, in particular the cavities of concave

obstacles. To do so an imaginary line is drawn, start-

ing from the robot’s position and going through the

obstacle blocking its path. As soon as enough free

space is found, a goal point is located.

3 EXPERIMENTAL SETUP

We implemented our strategy on the EyeBot-platform

within a simulator called EyeSim (A. Koestler and T.

aunl, 2004). In addition we built a generic test suite

providing easy testing, conﬁguration and evaluation.

The setup comprises several robots and one computer

acting as a server. The robots are fully autonomous

and can communicate both directly with each other

and with the server (wireless).

3.1 EyeBotServer

The server has several tasks. Firstly, it is responsi-

ble for the conﬁguration of each experiment. It reads

in a conﬁguration-ﬁle containing parameters like the

shape of the formation, driving speed, width of slices,

etc. These parameters are sent to all robots which

means conﬁguration is centralized and the need to re-

compile the robots program is eliminated. It also al-

lows for easy extending the suite with other forma-

tions and strategies resulting in a very generic plat-

form. The second task of the server originates from

the exploration of an environment. It is responsible

for storing the global map of the area, constructed

by the robots. We do this to adjust for the limited

memory capacity of the robots and to provide a safer

storage of the map. Since a computer has a large

amount of memory, we use a grid to model the envi-

ronment. A grid makes it easy to calculate goal points

and the shortest route towards them. The last task of

the server comprises exporting the results of the ex-

ploration. A visualization of the map via a PNG im-

age is generated from the grid structure and stored on

disk.

3.2 EyeXplore

EyeXplore is the main program run by each EyeBot

and is an implementation of the deliberative strat-

egy described in section 2. We emphasize that the

robots running this program are fully autonomous in

the sense that they don’t get instructions from the

server. For them the server acts as an extended mem-

ory in which the map can be stored. During explo-

ration, the robots are responsible for determining the

edges of obstacles which are then send to the server.

The robots don’t store these edges permanently in lo-

cal memory in order to save space. There is thus no

distributed map among the robots. In order to explore

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(a) Exploring robots in front of different obstacles.

(b) In case of larger obstacles, the two exploring robots might explore the same obstacle.

When they actually meet each other during wall-following, they know there is only one

obstacle. Both robots then only explore half of the obstacle’s border to gain speed.

Figure 1: Illustration of exploration phase. Two explorer robots are selected and perform a wall-following.

(a) (b) (c)

Figure 2: Robot collisions during wall-following to reach goal-point.

Figure 3: PSD sensors used during the experiments.

the edges of obstacles and to avoid collisions, we use

distance sensors as shown in ﬁgure 3.

4 RESULTS

In this section we discuss some results of the delib-

erative strategy. We examine the exploration time for

ﬁve different environments (shown in ﬁgure 4). Due

to page restrictions only a single map is shown.

In order to examine the inﬂuence of the number

of robots on the exploration time, each environment

was explored ﬁve times. For each test the number of

robots was increased with one, starting from a sin-

gle robot until a maximum of ﬁve robots. This gives

us ﬁve durations in total for each environment (third

column of ﬁgure 4). From the results it is clear that

introducing a second robot always leads to a faster ex-

ploration with respect to a single robot. This is caused

by a higher parallelization and a bisection of the num-

ber of slices required to explore the area. Bringing

in even more robots doesn’t always have the same

positive effect. This observation has several causes.

Firstly, remark that the extra decrease in the number

of required slices declines as the number of robots in-

creases. Another cause is that within our strategy, at

most two robots are exploring new obstacles at the

same time. The other robots are stalled during that

time, which can lead to relatively long delays depend-

ing on the obstacles within the environment. In ﬁgure

4 the large S-shaped obstacle is explored by all robots

when the formation contains only two robots. Adding

a third robot causes this obstacle to be explored by

just one robot, introducing delays for the remaining

two robots. This explains the large increase of explo-

ration time. Also note that the obstacles in ﬁgure 4 are

placed such that the formation cannot be maintained

as soon as there are more than two robots, since there

is not enough space to form a line. This also means

the robots will hamper each other more often which

has a negative effect on exploration time.

We also examined the inﬂuence of the width of the

formation and thus the width of the slices. For this

purpose each environment was explored ﬁve times

once more. Now each test is done by a forma-

tion comprising a constant number of robots, namely

MAP EXPLORATION USING A LINE-BASED FORMATION OF MOBILE ROBOTS

243

Figure 4: Results for a difﬁcult map (4 other maps are not shown due to page restrictions). Four columns are shown for each

environment. From left to right: the simulator, the resulting PNG image of the map, exploration time with increasing number

of robots and ﬁnally with an increasing slice width.

three. Instead, we vary the horizontal distance be-

tween each two neighboring robots standing in a line

formation. The smallest distance is 33 cm and this

is increased with 10 cm in each step, corresponding

to the width of a single EyeBot. The largest distance

between two robots considered here is thus 73 cm.

The exploration times for each environment are

shown in the fourth column in ﬁgure 4. Note that

the graphs now show a more clear descending ten-

dency and exploration times are lower than when we

increased the number of robots. This is caused by

the decreasing number of slices as we increase slice

width. Fewer delays are introduced as well because

for most of the test-environments two robots were ex-

ploring at the same time, implying more paralleliza-

tion. The robots also don’t hinder each other as often

as with a formation comprising four or ﬁve robots.

Therefore we can assume it is better to ﬁrst increase

slice width in order to lower exploration times. Of

course the range of the sensors and communication

hardware will limit the maximum width and eventu-

ally additional robots should be used as well.

5 CONCLUSIONS AND FUTURE

WORK

We developed a deliberative strategy to explore an un-

known environment with a formation of robots. Both

concave and convex obstacles are supported, which is

the primary innovation of our research. Moreover, the

strategy is very scalable as it can be applied from a

single robot to any number of robots (as long there

is enough space in the environment). The results

from our tests are very promising, yet further research

is still needed to exploit the number of robots even

more.

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