SURVEILLANCE SYSTEM USING A MOBILE ROBOT EMBEDDED

IN A WIRELESS SENSOR NETWORK

Syed Irtiza Ali and Baerbel Mertsching

GET Lab, Institute of Computer Science, Electrical Engineering and Mathematics, University of Paderborn

Pohlweg 47-49, 33098, Paderborn, Germany

Keywords:

Mobile robot, Localization, Robot mapping, Path planning, Wireless sensor network.

Abstract:

In this paper, we proposed a surveillance system for guard robots to perform indoor navigation using a wireless

sensor network. The aim is to provide a generic surveillance solution for multiple indoor scenarios. The

multi-sensor based localization method for the robot has been employed to overcome the shortcomings of

the standard AMCL based localization technique. It is also helpful in dealing with sensor limitations. The

proposed strategy has been implemented and tested within lab environments. The results show a fair reduction

in processing time required by convergence of localization process.

1 INTRODUCTION

The motivation of this paper is to present a generic

surveillance strategy for different indoor environ-

ments (ofﬁces or museums) by making use of a

mobile robot and a static wireless sensor network

(WSN). The proposed system consists of surveying

an entire ﬂoor. The ﬂoor consists of different rooms

and every room is equipped with at least one motion

detector which is combined with a radio transmitter.

A mobile robot carries a receiver node and a map of

the environment and it resides in one of the rooms.

In case of an intrusion the activated motion detec-

tor generates an alarm via the network and brings the

robot into action. The robot being in an autonomous

mode, navigates to the location of the detected in-

truder where it is switched to the teleoperated mode.

A human safeguard (e. g. in another building) can

make use of the robot’s camera to identify the intruder

who may be an employee or a burglar. In future such

robots may be equipped to tackle the intrusion as well.

In the scenario the robot utilizes the principle of

simultaneous localization and mapping (SLAM). The

localization process is a prime issue in the ﬁeld of

autonomous mobile robots. Its accuracy depends on

the preciseness of sensors and the exactness of the

map. There exist different methods to estimate the

position of a mobile robot for indoor and outdoor

scenes. Global positioning system (GPS) is a standard

method to localize a mobile robot in outdoor scenes

but this turns out to be inefﬁcient for indoor scenar-

ios due to a poor reception of GPS signals. There-

fore we have to look for an alternate technique for

localization in our problem domain. In this regard,

the term SLAM addresses a dependency of the map-

ping on the localization and vice versa. It has been

comprehended in sufﬁcient detail in various robotics

literature (see (Durrant-Whyte and Bailey, 2004) and

(Fresse, 2006)). The SLAM solutions based on parti-

cle ﬁlter effectively deal with non-linearities existing

in environments (see (Arulampalam et al., 2002) and

(Montemerlo et al., 2003)). Therefore, we have se-

lected a particle ﬁlter based SLAM solution to imple-

ment our strategy. Normally, most of these techniques

make use of sensor readings and then apply Bayesian

classiﬁer based calculations to perform localization.

It is safe to presume here that SLAM methods based

on particle ﬁlter yield the acceptableresults in speciﬁc

scenarios provided a precise a priori map of an envi-

ronment is available. However, sensors are subjected

to noise in the environment resulting in an imprecise

perception. Therefore it is appropriate here to use a

multi-sensor based localization solution as presented

in (Castellanos and Tardos, 1999), (Wu and Johnson,

2008) and (Wold et al., 2002) instead of relying on

a single sensor. This approach performs localization

by fusing data received from different heterogeneous

sensors. Navigation is another important aspect in the

ﬁeld of an autonomous mobile robots and it mainly

aims at reaching a particular location while avoiding

both dynamic and static obstacles.

The collaborative use of a mobile robot and WSN

293

Irtiza Ali S. and Mertsching B. (2009).

SURVEILLANCE SYSTEM USING A MOBILE ROBOT EMBEDDED IN A WIRELESS SENSOR NETWORK.

In Proceedings of the 6th International Conference on Informatics in Control, Automation and Robotics - Robotics and Automation, pages 293-298

DOI: 10.5220/0002215102930298

 SciTePress

had been previously presented with different aspects.

(Batalin et al., 2004) have discussed a mobile robot

navigation using a sensor network. A mobile robot

receive signals from different sensor nodes and de-

cides which sensor node is nearest. It then performs

localization. This method clearly obviates the use of

a known map but the accuracy of estimation is poor.

An interesting application scenario to perform the ﬂy-

ing robot navigation using sensor network has been

discussed in (Corke et al., 2005). The next section

discusses the surveillance strategy.

2 SURVEILLANCE STRATEGY

The generic surveillance strategy is shown in the ﬁg-

ure 1 which illustrates a multiple room scenarios.

This work focuses mainly on building a robust and

the reliable surveillance system for an indoor envi-

ronments. Therefore, we have utilized standard tech-

niques for path planning and navigation (local and

global) whereas the process of localization is im-

proved using a multi-sensor localization process.

As shown in ﬁgure 1 every room is deployed with

radio transmitters R

. The radio transmitters are con-

nected to motion detectors and generate an alarm via

the network upon the detection of any physical in-

trusion. The alarm message contains the transmit-

ter node identiﬁcation number and its position. The

robot receives a wake-up call upon detection of intru-

sion and start to estimate its location using a multi-

sensor localization process. The shortest path from

the robot’s current position to the target position of

sensor node is generated using a wavefront based path

planning technique. The robot then navigates toward

the goal position while avoiding obstacles and per-

forms visual sensing upon reaching its goal. The

next sections provide a detailed discussion on differ-

ent modules.

2.1 Establishing a Wireless Sensor

Network

The ﬁrst step involves the establishment of wireless

sensor network. The j=n-2 of total n radio nodes

are deployed in an area which the robot moni-

tors. They are static and connected to motion de-

tectors. The rest of the two nodes are attached to a

mobile robot and a console PC. A mobile robot re-

ceiver picks up status messages generated by static

transmitter nodes. The network is established among

radio transmitters using a table based routing scheme.

Each transmitter sends a beacon message to its neigh-

boring nodes which acknowledges the beacon mes-

Figure 1: Generic surveillance strategy of a mobile robot.

sage with routing table messages. This is necessary to

provide information about the next possible hop. Af-

ter an exchange of acknowledgement messages, each

node then broadcasts node detected messages to up-

date the routing table. Each motion detector has its

deﬁned vicinity in which it can detect the movement

of different physical objects. Whenever a movement

is sensed by a transmitter node, it generates an alarm

which is received both by a mobile robot and the con-

sole PC. The alarm contains an identiﬁcation number

of the transmitter node. The position of the transmit-

ter node on a priori map is identiﬁed by its number.

Figure 2 displays a graphicaluser interface (GUI) run-

ning on the console PC. The placement and status of

each radio transmitter node in an established sensor

network can be monitored through this designed GUI.

It is dynamic in a sense that it is capable to load a

map of a new operational environment and can adjust

the placement of the sensor nodes in the new environ-

ment.

2.2 Localization: Pose Estimation of a

Mobile Robot

Once an alarm is generated, the next step is to esti-

mate the location of a mobile robot which is assumed

to be unknown in the begining. In the current appli-

cation, the presence of a noise in the sensor models

and the imprecise actuator control of mobile robots

makes it difﬁcult to accurately estimate the robot posi-

tion even for the known environments. A fast conver-

gence of the estimated position is another challenge

in the ﬁeld of localization. In this regard, we propose

a multi-sensor based localization scheme to deal with

different issues faced during the position estimation

ICINCO 2009 - 6th International Conference on Informatics in Control, Automation and Robotics

294

Figure 2: Graphical user interface on the console PC.

of a mobile robot in an indoor environments. A mo-

bile robot performs the localization using an adaptive

Monte Carlo localization (AMCL) technique (Pfaff

et al., 2006). It is applicable to both local and global

localization problems. It is simply a variant of parti-

cle ﬁltering (Arulampalam et al., 2002). It makes use

of the recursive Bayesian ﬁltering scheme in order to

estimate a mobile robot location in an environment. It

also requires a decent sensor model and the motion

model of a mobile robot. It is worth mentioning here

that AMCL is very much capable to handle complex,

multimodal (non-Gaussian) posterior distributions of

a mobile robot locations. However, it has difﬁculties

when the pose of a mobile robot is high dimensional

because the number of particles increases exponen-

tially with the dimensionality of state space and hence

increases the computational complexity of the overall

process. This is yet an open research issue in the ﬁeld

of SLAM and the human body tracking applications.

AMCL estimates the pose from an input data of

an odometry sensor and a laser range ﬁnder. AMCL

requires an accurate sensor model but it is difﬁcult to

design a perfect sensor for the varied environments.

For example there are cases where a laser range ﬁnder

fails to provide the reliable range data, especially in

the presence of glass windows or doors. The presence

of the bright light and the vibrations produced by a

mobile robot also affect the performance of a laser

range ﬁnder. In order to deal with these problems, we

have implemented a multi-sensor based localization

strategy as shown in ﬁgure 3. The scheme is inspired

by the multi-sensor data fusion techniques presented

in (Castellanos and Tardos, 1999) and (Wu and John-

son, 2008) with an aim to deal with drawbacks of a

laser range ﬁnder with an added advantage of lower-

ing the convergence time during pose estimation pro-

cess. The main steps involve are:

Figure 3: A Multi-sensor localization process.

1. Exploration of the Environment. A mobile

robot explores an area using a monocular web

camera mounted on a mobile robot.

2. Recognition of Landmarks. We have selected

a segmentation method provided by (Aziz and

Mertsching, 2006) in order to recognize the dif-

ferent landmarks. The segmentation is reliable for

SURVEILLANCE SYSTEM USING A MOBILE ROBOT EMBEDDED IN A WIRELESS SENSOR NETWORK

295

Figure 4: Left: original images and right: segmented out-

put.

indoor scenarios. Figure 4 displays different land-

marks and the segmented output. The landmarks

are placed at known positions.

3. Position of the Landmark in a Map. The de-

tected landmark is matched with known land-

marks to determine its global position coordinates

on the map.

4. Position of a Mobile Robot in a Map. The pose

of the robot is calculated using the landmark’s po-

sition and distance information obtained through

a laser range ﬁnder. The procedure is explained in

the subsequent section.

5. AMCL. The robot pose is provided to the AMCL

localization module as an initial position of a mo-

bile robot to estimate its actual pose in the map.

6. Relocalization. Steps 1-5 are repeated whenever

the robot position is lost during navigation appli-

cations.

2.2.1 Orientation of Robot

Figure 5 illustrates the method to ﬁnd out the orien-

tation of the robot θ

relative to a priori map in the

world coordinates with following details:

1. The pose information of the wall (x

wall

, y

wall

) relative to the world coordinates is known.

2. The orientation of the camera θ

cam

relative to the

robot coordinates is also known.

3. The landmark is detected such that it should be in

the middle of an acquired visual input. A laser

range ﬁnder and a camera both are identically ori-

ented with respect to robot coordinates.

4. The angles θ

and θ

are computed by adding

and subtracting 10 degrees to θ

cam

5. The distances d

and d

are then calculated using

a laser range ﬁnder for θ

and θ

respectively.

6. The angle θ

w−r

is calculated to ﬁnd out the wall

to a mobile robot orientation.

w−r

= arctan

cosθ

− d

cosθ

sinθ

− d

sinθ

(1)

7. Finally, the orientation of a mobile robot relative

to the wall θ

is computed using:

= θ

wall

− θ

w−r

(2)

Finally a mobile robot location relative to a priori

map is found out using θ

and range information of

landmark.

Landmark

LaserFoV:240

world

-y

w-r

wall

Wall

cam

world

A mobile robot

Figure 5: Pose estimation of a mobile robot in a priori map

using multi-sensor localization.

2.3 Navigation

After estimating the global position of a mobile robot

in a known map, the next step is to navigate a mobile

robot to a goal location. The whole procedure of nav-

igation is shown in ﬁgure 6. When any of the radio

transmitters detects a motion in its vicinity, it gener-

ates an intruder alarm signal. It is then broadcasted to

other wireless sensor nodes. This alarm signal pro-

vides the identiﬁcation number (IN) of a particular

transmitter node which generated an alarm. The IN

is then found out using a table which contains a list

of coordinate position of different transmitter nodes

and this position is considered as a goal location for

the navigator module. The goal location is needed

to ﬁnd a desired path from the actual position of the

robot. The desired path is calculated using a path

planning algorithm. Path planning is a well discussed

topic in the ﬁeld of a mobile robot navigation. We

have selected the wavefront path planning algorithm

(Behring et al., 2000) due to its suitability with grid

based maps. The wavefront planning algorithm cal-

culates a list of waypoints between a mobile robot

position and a goal location. This list is necessary

to generate the shortest possible path among them af-

ter taking into account the size of the robot and a safe

distance from different obstacles. Once the success-

ful path is discovered, the next step is to navigate a

mobile robot between its present location and a goal

ICINCO 2009 - 6th International Conference on Informatics in Control, Automation and Robotics

296

Figure 6: Navigation process.

by making use of the robust path following method.

This also requires an integrated obstacle avoidance

behavior for both the static and the dynamic obsta-

cles. Normally, the list of static obstacles is known

through an a priori map while a well deﬁned local

navigation strategy is required to deal with dynamic

obstacles. There are different methods to perform this

kind of navigation. We have utilized the nearness di-

agram (Minguez and Montano, 2004) approach. This

approach provides a good methodology to avoid static

as well as dynamic obstacles for indoor environments.

The robot then follows a generated path and navigates

safely toward its goal. Once it reaches the goal loca-

tion, it generates a goal reached message. There is a

provision to perform an exploration of the goal loca-

tion surroundings with the help of an available visual

sensor. This also enables a console PC to monitor a

suspected area. The experiments and their outcomes

are discussed in the next section.

3 EXPERIMENTS AND

DISCUSSION

The experiments are conducted at GET lab, Univer-

sity of Paderborn. We used the customized Pioneer

3AT robot GETbot equipped with the two dimen-

sional and a 240 degreeﬁeld of view laser range ﬁnder

(Hokuyo, 2009) and the pan-tilt based webcam for ex-

ploring an environment and avoiding the obstacles.

Tmote sky sensor nodes (Tmote, 2009) are used as

wireless nodes in a static WSN. They are attached

to the motion detector. The map of the experimen-

tal setup and a pre-deployed static WSN of 6 nodes

are shown in ﬁgure 2. It is a regular ofﬁce environ-

ment with narrow door openings of about 90 cm. The

nodes are deployed on the ceiling. Figure 1 shows

the experimental mobile robot being employed. The

surveillance strategy is implemented using a player-

stage (Collet et al., 2005) robot control toolkit.

Figure 7: Robot localization using standard AMCL (a) T=0

s and particles=10,000. (b) T=10 s and particles=5000. (c)

T=15 s and Particles=1000. (d) T=20 s and particles=100.

The experiments are performed on the basis of

event handling and the generation of an alarm is con-

sidered as an event. The two different navigation be-

haviors are generated according to following condi-

tions:

1. The ﬁrst alarm received by a mobile robot is con-

sidered as the priority alarm which is then locked

to reach the goal location. The alarms received

afterwards are considered as the false alarms.

2. The last alarm received by a mobile robot is con-

sidered as a priority alarm.

A mobile robot reaches its goal location area with an

accuracy of 0.25 meters. It is set as a parameter during

the path computation step. The availability of an exact

map of an environment increases the robust behavior.

The results of localization performed using standard

AMCL is shown in ﬁgure 7. The initial position of a

mobile robot is unknown. Once a mobile robot starts

its localization process, it begins matching the scans

obtained through a laser range ﬁnder with a known

map of an environment and awards high weightage to

most probable matched places. In this way, a mobile

robot tends to localize itself to most probable position

over the time. The exact match sometimes takes up

to several seconds to ﬁnd an accurate estimation of a

mobile robot pose as shown in ﬁgure 7. Symmetric

environments are prone to false position estimations.

In order to avoid these problems we have placed land-

marks as shown in ﬁgure 4 at different places in the

experimental scenario. It is important to highlight that

the size and placement of landmarks is an important

factor. The landmarks must be placed at the height of

a laser range ﬁnder. This is advantageous in avoiding

collisions with glass doors which are otherwise not

SURVEILLANCE SYSTEM USING A MOBILE ROBOT EMBEDDED IN A WIRELESS SENSOR NETWORK

297

detected by the employed laser range ﬁnder. The posi-

tions of landmarks on the map are known. The visual

sensor is then utilized to perform a multi-sensor based

localization process. This is quite useful in estimating

a mobile robot pose on the map. It also speeds up the

convergence process of the localization from around

20 seconds to 10 seconds. However, it depends on a

good landmark detection scheme. It has been tested

that this methodology works quite efﬁciently when-

ever the robot needs to perform self re-localization in

wake of position loss. Overall this results into a more

reliable and the efﬁcient navigation behavior.

4 CONCLUSIONS AND FUTURE

DIRECTIONS

We have presented a generic surveillance strategy for

a guard robot using a wireless sensor network. The

scheme has been implemented and worked out for

different indoor scenarios. Our approach presents an

improved localization process by employing a multi-

sensor localization technique. It also allows the in-

tegration of different sensors to deal with different

kinds of environment. The results show that a fast

convergence of the localization process is achieved

while effectively reducing the effects of a sensor

noise. In the future work, we intend to see how the

system performs reliably providing a relaxation in as-

sumed conditions and parameters. The detailed com-

parison of a proposed localization strategy with other

standard techniques is also an immediate step.

ACKNOWLEDGEMENTS

The authors would like to thank the higher education

commission of Pakistan and the DAAD of Germany

for supporting the research studies at University of

Paderborn, Germany. The authors would also like to

thank Mr. D. Fischer, Mr. M. Z. Aziz and Mr. S.

Shaﬁk for providing useful comments.

REFERENCES

Arulampalam, S., Maskell, S., Gordon, N., and Clapp,

T. (2002). A tutorial on particle ﬁlters for on-

line nonlinear/non-gaussian bayesian tracking. IEEE

Trans. on Signal Processing, 50(2):174–188.

Aziz, M. Z. and Mertsching, B. (2006). Color segmentation

for a region-based attention model. In Proceedings of

12th Workshop Farbbildverarbeitung, pages 74–83.

Batalin, M. A., Sukhatme, G. S., and Hatting, M. (2004).

Mobile robot navigation using a sensor network. In

Proceedings of IEEE Intl. Conf. on Robotics and Au-

tomation, pages 636–641.

Behring, C., Brancho, M., Castro, M., and Moreno, J.

(2000). An algorithm for robot path planning with

cellular automata. In Proceedings of Intl. Conf. on

Cellular Automata for Research and Industry, pages

11–19.

Castellanos, J. A. and Tardos, J. D. (1999). Mobile robot

localization and map building - A multi-sensor fusion

approach. Kluwer Academic Publishers.

Collet, T. H. J., MacDonald, B. A., and Gerkey, B. P. (2005).

Player 2.0 : Toward a practical robot programming

framework. In Proceedings of Australian Conf. on

Robotics and Automation.

Corke, P., Peterson, R., and Rus, D. (2005). Networked

robotics: Flying robot navigation with a sensor net-

work. Springer Tracts in Advanced Robotics, 15:234–

243.

Durrant-Whyte, H. and Bailey, T. (2004). Simultaneous lo-

calization and mapping (slam): Part i the essential al-

gorithms. Robotics and Automation Magazine, 13:99–

110.

Fresse, U. (2006). A discussion of simultaneous localiza-

tion and mapping. Autonomous Robots, 20(1):25–42.

Hokuyo (2009). Laser range ﬁnder URG-LX04.

www.hokuyo-aut.jp/02sensor/07scanner/urg.html,

site accessed: 22.02.2009.

Minguez, J. and Montano, L. (2004). Nearness diagram

navigation (nd) : Collision avoidance in troublesome

scenarios. IEEE Trans. on Robotics and Automations,

20(1):45–59.

Montemerlo, M., Thrun, S., Koller, D., and Wegbreit, B.

(2003). Fastslam 2.0 : An improved particle ﬁltering

algorithm for simultaneous localization and mapping

that provable converges. In Proceedings of Intl. Joint

Conf. on Artiﬁcial Intelligence, pages 1151–1156.

Pfaff, P., Burgard, W., and Fox, D. (2006). Robust monte-

carlo localization using adaptive likelihood models.

In European Robotics Symposium, pages 181–194.

Springer-Verlag Germany.

Tmote (2009). Tmote sky nodes IEEE 802.15.4.

www.sentilla.com/pdf/eol/tmote-sky-datasheet.pdf,

site accessed: 22.02.2009.

Wold, J., Burgard, W., and Burkhardt, H. (2002). Robust

vision-based localization for mobile robots using an

image retrieval system based on invariant features. In

Proceedings of IEEE Intl. Conf. on Robotics and Au-

tomation, pages 359–365.

Wu, A. D. and Johnson, E. N. (2008). Methods for localiza-

tion and mapping using vision and inertial sensors. In

Proceedings of AIDA Guidance, Navigation and Con-

trol Conf. and Exhibit. AIAA 2008-7441.

ICINCO 2009 - 6th International Conference on Informatics in Control, Automation and Robotics

298