A DISTRIBUTED MULTI-ROBOT SENSING SYSTEM USING AN

INFRARED LOCATION SYSTEM

Anssi Kemppainen, Janne Haverinen and Juha R

oning

Department of Electrical and Information Engineering, University of Oulu, Finland

Keywords:

Distributed sensing, relative pose estimation, multi-robot system, formation control.

Abstract:

Distributed sensing refers to measuring systems where, instead of one sensor, multiple sensors are spatially

distributed to improve the robustness of the system, increase the relevancy of the measurements and cut costs,

since smaller and less precise sensors are used. Spatially distributed sensors fuse their measurements into the

same co-ordinates, which requires the relative positions of the sensors. In this paper we present a distributed

multi-robot sensing system in which the relative poses (positions and orientations) of the robots are estimated

using an infrared location system. The relative positions are estimated using intensity and bearing measure-

ments of received infrared signals. The relative orientations are obtained by fusing the position estimates of

the robots. The location system enables a group of robots to perform distributed and co-operative environ-

ment sensing by maintaining a given formation while the group measures distributions of light and a magnetic

ﬁeld, for example. In the experiments, a group of three robots moved and collected spatial information (i.e.

illuminance and compass headings) from the given environment. The information was stored on grid maps

that present illuminance and compass headings. The experiments demonstrated the feasibility of using the

distributed multi-robot sensing system in mobile sensing applications.

1 INTRODUCTION

Distributed sensing (Brooks and Iyengar, 1998), (Cat-

terall et al., 2003) refers to measuring systems where,

instead of one sensor, multiple sensors are spatially

distributed to improve the robustness of the system,

increase the relevancy of the measurements and cut

costs, since smaller and less precise sensors are used.

In the robotic domain, distributed sensing enables

multi-robot systems (MRS) to perform mapping and

exploration (Sujan et al., 2004), allocate tasks among

robots (Pagello et al., 2006), and plan paths and navi-

gate in an unknown or partially unknown environment

(Cai et al., 1996), for example.

Distributed sensing in an unknown enviroment

beneﬁts from knowing the relative locations of sen-

sors. The relative locations enable the sensors to place

their measurements into the same sensor-centric map.

Autonomous sensing systems are able to use one sen-

sor as an origin of the co-ordinates, while the other

sensors measure the surrounding area. Such systems

do not require an external infrastructure for position-

ing (GPS, WLAN, etc.). However, the measurement

range of the relative location system must be sufﬁcient

to encompass the sensing area.

In this paper we present a distributed multi-robot

sensing system that uses an infrared location system

(Kemppainen et al., 2006). The location system es-

timates the relative poses (positions and orientations)

of the robots. Related systems have been presented

that exploit several techniques, including laser range

ﬁnders (Schneider and Wildermuth, 2004), (Mon-

tesano et al., 2004), (Howard et al., 2003),(Moors

et al., 2003), ultrasonic TOF measurement (Shoval

and Borenstein, 2001), (Grabowski et al., 2000) and

vision (Montesano et al., 2005), (Spletzer et al.,

2001), to locate and recognise other robots. A com-

parison of the infrared location system and related

systems was discussed in (Kemppainen et al., 2006).

We selected infrared sensors since they are small and

capable of relative angle measurements between an

emitter and receiver. In addition, infrared radiation

280

Kemppainen A., Haverinen J. and Röning J. (2007).

A DISTRIBUTED MULTI-ROBOT SENSING SYSTEM USING AN INFRARED LOCATION SYSTEM.

In Proceedings of the Fourth International Conference on Informatics in Control, Automation and Robotics, pages 280-283

DOI: 10.5220/0001648502800283

 SciTePress

does not reﬂect from walls and object surfaces as

much as ultrasound. In relative angle measurements,

the effects of multipath reﬂection would be crucial be-

cause of ambiguous angle estimates.

To demonstrate the distributed sensing system in

mobile sensing applications, we conducted experi-

ments where a group of three robots measured the dis-

tributions of illuminance and a magnetic ﬁeld while

maintaining a triangle formation.

2 INFRARED LOCATION

SYSTEM

The infrared location system, originally presented in

(Kemppainen et al., 2006), is a vital part of the multi-

robot system (Haverinen et al., 2005) that enables the

robots to maintain a given formation while sensing the

environment. The key idea of the location system is to

estimate the relative positions without data transmis-

sion between the robots. However, radio transmission

is used to share estimates among the group in order

to enable the robots to estimate their relative orienta-

tions.

The location system uses intensity and bearing

measurements of received signals to estimate the lo-

cations of other robots in polar co-ordinates. By shar-

ing these estimates among the group of robots, it is

possible to also estimate their relative orientations. In

addition, each robot is identiﬁed through different fre-

quencies in the received signal.

Figure 1 presents the main components of the lo-

cation system. A conical mirror reﬂects an emitted

signal sideways into a uniﬁed zone, whereas beam

collector collects signals from other robots. A servo

system with a DC motor, Hall-effect sensors and dis-

crete PID controller is used to rotate the beam collec-

tor at a constant angular velocity. The measurement

range of the system is approximately ﬁve metres, giv-

ing the most accurate estimates for radial co-ordinates

when the distance between two robots is in the range

of [0.5; 2.5 m].

3 EXPERIMENTS

Experiments were conducted to demonstrate the fea-

sibility of using the distributed multi-robot sensing

system in mobile sensing applications. For exam-

ple, co-operative mapping in an unknown environ-

ment requires moving platforms that are capable of

measuring spatial information and estimating the rel-

ative positions of the robots. For our experiments, we

Figure 1: The actual system and the illustration of mechan-

ics: 1) mirror, 2) emitter, 3) receiver, 4) beam collector,

5) aperture, 6) DC motor and Hall-effect-sensors, 7) see-

through body, 8) control electronics.

implemented a distributed sensing system in which a

group of three robots measured spatial information in

a given environment.

3.1 Formation Control

Formation control enables a multi-robot system to

measure spatial distributions while moving across the

measurement area. The measurement range of the

infrared location system is restricted to ﬁve metres,

and to be able to fuse spatial measurements onto the

same map, the multi-robot system is required to main-

tain limited relative distances. Formation control is

required not only to limit the distance between the

robots, but also to enable co-ordinated sensing in or-

der to reduce mapping time.

In our experiments one of the robots acted as a

leader and the other two followed the leader. To-

gether they constituted a right-angle triangular forma-

tion where the distance between the leader and the fol-

lowers was 1.5 metres. For each following robot, for-

mation control consisted of two P controllers; one for

translational and the other for rotational speed con-

trol. The infrared location system updated the relative

pose measurements approximately every three sec-

onds, giving the relative poses of the following robots

in the leader’s co-ordinates. Fig. 2 presents how er-

rors in the relative orientation and position were used

in the rotational and translational speed controllers

(respectively).

The control cycle length in each controller was

200 ms, while the location system updated poses only

once every three seconds. In order to update pose er-

rors in each control cycle, each robot estimated their

relative movements using odometer readings. In the

experiments, the odometer readings enabled the for-

mation to be driven at a 10 cm/s translational velocity.

A DISTRIBUTED MULTI-ROBOT SENSING SYSTEM USING AN INFRARED LOCATION SYSTEM

281

The following robot

The leader robot

Objective position

t

e

r

e

Figure 2: The pose of the following robot relative to the

leader is estimated and used to control the robot to the ob-

jective position. P controllers use rotation e

and translation

errors to control rotational and translational speeds of the

robot.

3.2 Spatial Measurements

In the experiments, three robots measured distribu-

tions of light and a magnetic ﬁeld over a given en-

vironment, producing maps of illuminance and com-

pass headings. Fig. 3 presents the trajectories of the

robots while moving and measuring the environment.

The leader was driven from position (50 cm, -250 cm)

to position (20 cm, 450 cm), while the group main-

tained a triangular formation. Spatial measurements

were stored on grid maps presented in the leader’s co-

ordinates.

−400−300−200−100 0 100 200 300 400 500

−400

−300

−200

−100

100

200

300

400

500

X coordinate (cm)

Y coordinate (cm)

Trajectories

Figure 3: Trajectories in global coordinates.

Fig. 4 presents a grid map of illuminance, where

the highest intensity is depicted with white colour and

the lowest intensity, for the cells not visited, with

black colour. This gives spatial information about the

distribution of light in the given environment. The

cells with the highest intensities are close to lights and

the cells with the smallest intensities are shadowed ar-

eas close to chairs, plants and walls.

−500 0 500

−500

−400

−300

−200

−100

100

200

300

400

500

Illuminance

X coordinate (cm)

Y coordinate (cm)

100

Figure 4: Illumincance in global coordinates.

Fig. 5 presents a grid map of compass headings

with a bidirectional arrow, where red points to the

north and white to the south. Since compass head-

ings are disturbed indoors by electric cables and metal

structures, the values of the compass headings give us

spatial information about the magnetic ﬁeld in the en-

vironment. However, in the experiments, the mag-

netic ﬁeld of the measured environment was paral-

lel, which gave us small spatial variations in compass

heading.

−400−300−200−100 0 100 200 300 400 500

−400

−300

−200

−100

100

200

300

400

500

X coordinate (cm)

Y coordinate (cm)

Compass heading

Figure 5: Compass heading in global coordinates.

The experiments demonstrated distributed sens-

ing in a group of robots to produce distributions of

illuminance and a magnetic ﬁeld. All the measure-

ments were tied to the co-ordinates of the leading

robot. Since the ground truth positions were miss-

ing, positioning errors in the global co-ordinates of

ICINCO 2007 - International Conference on Informatics in Control, Automation and Robotics

282

the sensing robots were affected by the odometer er-

ror of the leading robot and the errors of the infrared

location system. In addition, the grid size in the ex-

periments was 1x1 metres, which gave only a coarse

picture of the true distributions. However, these pre-

liminary experiments demonstrated the feasibility of

using the infrared location system in distributed au-

tonomous sensing systems.

4 CONCLUSION AND FUTURE

WORKS

In this paper we presented a distributed multi-robot

sensing system that uses an infrared location sys-

tem. The infrared location system estimates poses in

a multi-robot system, enabling the robots to maintain

a given formation while sensing the environment. In

addition, knowing their poses enables the robots to

place their measurements on the same map.

We conducted an experiment where a group of

three robots moved and measured spatial informa-

tion in a right-angle triangular formation. Leader-

follower formation control used pose estimates and P

controllers to control the rotational and translational

speeds of the following robots. In the experiments we

measured spatial distributions of illuminance and a

magnetic ﬁeld, which gave us information about shad-

owing objects, metal structures and electric cables. In

addition, since the information is spatially distributed,

it can be used in mapping and localization applica-

tions.

The main contribution of the research was the con-

struction and validation of a distributed multi-robot

sensing system for mobile sensing applications. Fu-

ture research will focus on developing methods for

multi-robot exploration utilising spatial information.

ACKNOWLEDGEMENTS

The authors gratefully acknowledge the support of the

Academy of Finland.

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