A SIMULATION SETUP FOR COMMUNICATION HARDWARE IN

THE LOOP EXPERIMENTS

Markus Sauer and Florian Zeiger

Zentrum f¨ur Telematik e.V., Allesgrundweg 12, Gerbrunn, Germany

Klaus Schilling

Department of Computer Science VII, University of W¨urzburg, Am Hubland, W¨urzburg, Germany

Keywords:

Networked mobile robots, Communication, Simulation.

Abstract:

Simulations are a very powerful tool in robotics to design and verify new algorithms before doing time-

consuming tests with real hardware. Nowadays, a lot of very realistic simulation environments are available to

simulate robot kinematics and dynamics and any type of multi-robot systems in a virtual physical environment.

Unfortunately, the communication in these simulations is often only considered in a very simpliﬁed matter,

although the characteristics of a real communication link are very complex and might have a strong inﬂuence

on the performance of a multi-robot algorithm. This contribution proposes a setup to perform communication

hardware in the loop tests with the 3D simulation environment USARSim. For this setup any communication

device which can be connected to a PC architecture like WLAN, UMTS or Bluetooth can be used. A coop-

erative collision avoidance algorithm is presented as an example which is realized with this setup, while real

hardware is used for the communication link between the robots. Finally, the limitations are presented.

1 INTRODUCTION

The progress in the area of telecommunication tech-

nology together with the demand of networked mo-

bile robot systems to assist humans in many different

areas (e.g. disaster management, security and surveil-

lance, or search and rescue applications) forces the

development of multi robot systems which incorpo-

rate several autonomy functions like formation driv-

ing and obstacle avoidance. Hereby, due to the re-

quired ﬂexibility and dynamic communication topol-

ogy, distributed control algorithms are very desirable.

For the development of these mechanisms to control

and coordinate swarms of mobile systems or multi

robot systems capable simulation or emulation en-

vironments are a useful and necessary tool for efﬁ-

cient development and analysis. But the use of simu-

lation environments for networked mobile robot sys-

tems also implies some consideration with respect to

signiﬁcance and validity of the simulation. On the

one hand the complete dynamics and kinematics of

each system must be modeled appropriately. On the

other hand, also the available communication link in-

between the robots must be represented in a suitable

manner. With respect to the simulation of the dynam-

ics and kinematics of mobile robots in multi robot sys-

tems several simulation environment were developed

in the recent years. Two well-known examples of the

many available simulators are Player/Stage (Gerkey

et al., 2003) and USARSim (Carpin et al., 2007).

Player is a robot device server to realize multi-robot

or sensor-network systems. Stage can be used to-

gether with Player and can simulate large populations

of robots in a 2D environment. USARSim is based on

the famous Unreal Tournament 2004 game engine. It

is a general purpose 3D - multi-robot simulator which

provides basic physical properties of the robot and

the simulated environment which closely match the

real implementation of the robots and the real envi-

ronment. In addition, it is also possible to simulate

camera images from cameras inside the simulation.

Compared to Player/Stage it is only a simulation with-

out a device server and controller concept like Player.

Figure 1 shows a typical environment simulated with

USARSim for the virtual RoboCup Rescue league.

With respect to the simulation of the communica-

tion link also many approaches and even products are

available to be integrated. Of course, the importance

of these simulations of communication link technolo-

gies is not only limited to the area of multi robot sys-

312

Sauer M., Zeiger F. and Schilling K. (2009).

A SIMULATION SETUP FOR COMMUNICATION HARDWARE IN THE LOOP EXPERIMENTS.

In Proceedings of the 6th International Conference on Informatics in Control, Automation and Robotics - Robotics and Automation, pages 312-317

DOI: 10.5220/0002218703120317

 SciTePress

Figure 1: Typical environment simulated for the virtual

RoboCup Rescue with USARSim.

tems. In the area of network testing and evaluation of

wireless network systems (Doshi et al., 2007) often

the QualNet network simulator is used for the setup

of real-time emulations. This simulator is also used

in (Xu et al., 2003) for simulations regarding quality

of service provisioning in wireless ad-hoc networks,

as well as in (Bagrodia et al., 2006), where a sys-

tems simulation environment for future combat sys-

tems is presented. In the area of networked haptic

virtual environments (Sankaranarayanan et al., 2007)

used NIST-Net to create realistic Internet-like charac-

teristics in a laboratory setting. NIST-Net (cf. (Car-

son and Santay, 2003)) is a tool to facilitate testing

and experimentation with network code through emu-

lation which can model communication performance

characteristics like packet delay, jitter, bandwidth lim-

itations, congestion, and packet loss.

Of course, there exist other powerful simulation

tools like NS2 or OPNET. All these simulation en-

vironments are very mighty tools which have focused

on the simulation of the characteristics of the commu-

nication channel. Unfortunately, they are often very

complex and time-consuming to operate and most of

them cannot be easily integrated with the simulation

environments for mobile robot dynamics and kine-

matics mentioned before. It is also known that the

simulation tool itself inﬂuences the outcome of a sim-

ulation (Liu and Kaiser, 2005). In addition, you need

to test the algorithm anyway later with real commu-

nication hardware. Currently, in the area of simula-

tion of networked robot systems and robot swarms

the simulation of the communication interface is of-

ten represented in a very abstract or simpliﬁed way.

Nevertheless, several publications for networked con-

trol systems turned out the importance of the knowl-

edge about the communication characteristics and its

inﬂuence on the implemented control algorithms. In

(Lopez et al., 2006), experiments of closed-loop net-

worked control systems are evaluated focusing specif-

ically on the performance and time delays effects

for different compensation actions. In (Wei et al.,

2001) stability of networked control systems is in-

vestigated for differentnetwork-schedulingprotocols.

Also methods for compensating network-induced de-

lay are presented together with experimental results

for networked control systems with packet loss on the

communication link. (Walsh et al., 2002) provided an

analytical proof of global exponential stability for a

novel control network protocol and commonly used

statically scheduled access methods. There, the fo-

cus is set on communication constraints which are

imposed by the network and the performance of the

proposed protocol and the statically scheduled pro-

tocols are examined in simulations. As above men-

tioned, the behavior of the communication channel is

very important for the analysis and implementation

of coordination and distributed control algorithms for

networked robotic systems and may inﬂuence the be-

havior of the complete system. Thus, this work pro-

poses an approach how real communication hardware

can easily be included into hardware simulation en-

vironments - in this case USARSim. The communi-

cation hardware is used as in real world applications

but nevertheless directly integrated to the algorithms

to be analyzed. The environment consisting in a map

and the dynamics and the kinematics of the physical

entities (mobile robot clients) is provided by the US-

ARSim server. This modular design allows ﬂexible

extensions in terms of replacing the simulated robot

hardware by real mobile robot hardware which accel-

erates the development duration of multi robot sys-

tems. As the proposed system integrates real wire-

less communication hardware and standard protocol

stacks directly into the simulation an intensive anal-

ysis of implemented coordination and control algo-

rithms for robot teams under consideration of the ef-

fects of real wireless communication is possible.

The remainder of this work is structured as fol-

lows. First, the hardware in the loop setup which

integrates the real communication stack in the sim-

ulation is introduced. Then an implementation of a

cooperative collision avoidance algorithm as example

application is presented. Afterwards, the areas and

the constraints of the proposed setup are investigated.

2 HARDWARE IN-THE-LOOP

SETUP

The objective of the simulation system design is the

use of real communication hardware while simulating

multiple robots with USARSim. Therefore, the pre-

sented system can be divided into three main parts: a

local area network segment, the clients, and a wireless

communication segment (cf. Figure 2).

A SIMULATION SETUP FOR COMMUNICATION HARDWARE IN THE LOOP EXPERIMENTS

313

Figure 2: Setup of Hardware in-the-loop Simulation Components.

2.1 Hardware Setup

The local area network segment uses standard Ether-

net communication to provide connectivity between

the USARSim server and all clients which supports

high bandwidth communication with low delays. This

connection is used for the exchange of drive com-

mands and sensor data between the USARSim simu-

lation environment and the different clients. This seg-

ment represents the indirect communication between

each client and its environment, and as it is realized

via the Ethernet segment, the direct communication

between each client over the wireless link is not inter-

fered. Communication between the clients is not im-

plemented via this link. The clients are equipped with

a mini PC architecture with 1200MHz, 1GB RAM,

a 8GB compact ﬂash card as hard disk, and Debian

Etch as operating system provides a platform to exe-

cute the programs and algorithms for navigation and

cooperation tasks which should be investigated and

analyzed. This mini PC represents the computing

power of a single robot. The LAN segment is only

used by each client to retrieve environment data from

the USARSim server. Communication between the

clients is only realized via the wireless communica-

tion segment. The wireless segment is based on IEEE

802.11 wireless LAN and represents the communi-

cation hardware which is directly integrated into the

simulation setup. This communication link is exclu-

sively used for the communication between all clients

i.e. robots and human operators. As a standard oper-

ating system is used the correspondingprotocol stacks

are available and also routing mechanisms for wire-

less ad-hoc networks like OLSR, DSR, or AODV can

easily be used.

2.2 Software Components

For each of the hardware components described in

Section 2 also dedicated software components are

existing. On the simulation layer a USARSim server

is running which provides an environment model,

the physical behavior of the clients, and sensor

data for clients. As only the Ethernet segment is

used for the communication between the USARSim

server and the clients, the inter client communication

via the wireless segment is not affected. On the

clients no speciﬁc installation for the USARSim

simulation and for maintaining the connection to the

USARSim server is required. The communication to

the USARSim server, and respectively the simulated

robots are realized with simple string messages over

TCP-socket (Carpin et al., 2007). Each client is

running on one of the described mini PC. Basically

here, the distributed control algorithms can be

implemented. Furthermore, the operating system

is also maintaining the communication link to the

USARSim server for sensor data acquisition and

sending commands. The client PCs are also equipped

with WLAN PCMCIA cards supporting the IEEE

802.11 b/g standard. The wireless communication

is exclusively used for inter-client-communication

which represents one of the key issues of the pro-

posed architecture. In the presented setup all standard

protocol versions which are available for the client

operating system (e.g. Debian Linux) can be used.

As the wireless communication link is exclusively

used for inter client communication, the real protocol

stacks and real physical behavior of the link allows

for meaningful hardware in-the-loop simulations.

Thus, the navigation, coordination and cooperation

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

314

algorithms which should be analyzed are exchanging

data via communication links with a realistic behav-

ior - including external disturbances.

2.3 The Simulation System Design

This system setup is designed as simulation envi-

ronment for network control systems and scenarios

of robots or robot formation driving with real IEEE

802.11 wireless LAN communication. In this work,

one client represents the formation leading robot and

the other three clients are robots which should keep

a certain formation. The leading robot sends its po-

sition data with a frequency of 10Hz to the other

team members via the wireless link. A communica-

tion from the team members back to the leader is not

present. The communication between the USARSim

server and each client uses the standard USARSim in-

terface based on TCP connections. All robots run the

same distributed cooperative collision avoidance al-

gorithm while moving to their respective goal points.

3 EXAMPLE: COOPERATIVE

COLLISION AVOIDANCE

Typically, distributed control algorithms for robotic

networks (Bullo et al., 2008) often assume a cer-

tain simpliﬁed model of the communication channel.

Here, a setup is proposed to test these control algo-

rithms with a real communication stack. As applica-

tion example for this contribution, a cooperative col-

lision avoidance control algorithms based on the con-

cepts of (Stipanovi´c et al., 2007) is used.

In the example scenario, a group of n mobile

robots should move through an environment without

colliding with objects in the environment or with each

other. There is no central instance coordinating the

movement of the robots. In the shown simulation mo-

bile robots with differential drive are used. They are

equipped with a simulated laser range ﬁnder for ob-

stacle detection. The laser range ﬁnder has a ﬁeld

of view of 180 degree and is mounted to the front

of the robots. The leader robot a

i=1

drives a rhom-

bus in this environment and continuously sends its

pose to the other following n− 1 robots. The robots

i∈{2..n}

receive this pose over the wireless communi-

cation segment and set their own new goal pose rela-

tive to the pose of the received leader pose. Thus, the

formation shown in Fig.3 is established in equilibrium

of the controller. Any other logical communication

topology can be realized with this kind of setup e.g.

robot one can only communicate with robot two and

robot three and four can only communicate with robot

two. This is especially interesting for the investigation

of the system behavior of distributed algorithms with

communication constraints.

Figure 3: Relative positioning of the robots in formation.

For the presented example application, the mobile

robots are modeled with the kinematics of a differen-

tial drive robot as ﬁrst order system (cf. equation 1);

˙x

= v

· cosΘ

˙y

= v

· sinΘ

= ω

(1)

, y

, and Θ

denote the pose of the robot i. v

the translational velocity and ω

the turn rate. On each

of the robots a combination of the following position

controller and a controller for obstacle avoidance is

implemented. The controller switches between dif-

ferent behaviors depending on the current conditions.

Without obstacles in the deﬁned obstacle avoidance

zone and the robot’s orientation is not towards the

goal (Θ

6= Θ

) the following controller applies:

= −(Θ

− Θ

) =

− u

)

⇒(u

− u

) = −

(Θ

− Θ

) (2)

denotes the radius of the i-th robots’ wheels, L is the

length between the wheels, u

is the left wheel speed,

is the right wheel speed respectively and Θ

is the

desired orientation towards the currently deﬁned goal.

If the robot is oriented towards the goal (Θ

−

) < t

- threshold for accuracy of orientation

of robot towards goal), v

is aligned with the straight

line between the robot’s position and the goal posi-

tion. Therefore, v

only applies to ˙x

in the robot co-

ordinate frame ˙x

= v and ˙y

= 0. The following con-

troller can be applied:

= −x

+ u

)

⇒u

= u

= −

Θ (3)

In the robot coordinate frame x

is under the above

given conditions equal to the distance between robot

and current goal and it becomes zero if the desired

A SIMULATION SETUP FOR COMMUNICATION HARDWARE IN THE LOOP EXPERIMENTS

315

goal is reached. If an obstacle is in the deﬁned sens-

ing range, the controller is adapted according to the

following rules: First a vector F

is calculated. F

points in opposite direction of the nearest obstacle to

the robot and its length increases indirect proportional

with the distance to the next obstacle. Then this vector

is combined with the normalized vector in goal direc-

tion F

to a new goal direction vector incorporating

an obstacle avoidance component and a new desired

heading Θ

oai

(cf. Figure 4):

oai

= (F

+ F

) (4)

oai

= arctan2(y

oai

, x

oai

) (5)

Finally, this value is inserted in the controller deﬁned

in Equation 2:

= −(Θ

− Θ

oai

) =

− u

)

⇒(u

− u

) = −

(Θ

− Θ

oai

) (6)

Figure 4: Overview of the different values for the obstacle

avoidance controller.

After orientation towards Θ

oai

the robot always

movesfor a small time period in this direction to avoid

oscillations in reorienting due to the limitation of the

obstacle sensing to 180 degrees. This translational

movement is only done in cases where deﬁnitely no

collision can occur.

The experiments with this cooperative collision

avoidance algorithm were done with n = 4 robots.

The results can be seen in Fig. 5 and Fig. 6. Fig.

5 shows how the four robots move with respect to

each other over time while the three robots follow the

leader robot driving a predeﬁned rhombus trajectory

for a certain experiment time. In each plane at a cer-

tain time the position of the robots at this times can be

seen. The 3D plot of the trajectories shows the reori-

entation of the formation at the edges of the rhombus

over time and it can be seen that there was now col-

lision because none of the trajectories is touching or

crossing each other. Fig. 6 shows the minimum dis-

tance inside the group of mobile robots. The relative

position of the three following robots was designed to

have a distance minimum of 1.7m between all robots

when they are moving in perfect formation. In ad-

dition the robots should never get closer then 0.4m.

Fig. 6 shows that the algorithm satisﬁes these require-

ments. The peaks in the graph occur always when the

formation is reorienting at the edges of the rhombus

driven by the leader robot.

−10

−5

−20

−10

0.5

1.5

2.5

3.5

x 10

x−position [m]

y−position [m]

time [ms]

Figure 5: Position of each robot while driving in formation.

0 0.5 1 1.5 2 2.5 3 3.5 4

x 10

0.5

1.5

2.5

3.5

time [ms]

minimum distance [m]

Figure 6: Minimum distance occurring between all robots

inside the formation.

4 APPLICATION AREAS AND

CONSTRAINTS

This simulation setup is designed for real communi-

cation hardware in-the-loop simulations of networked

robotics scenarios. The advantage of this special

setup is, that the real communication protocol stack

is used which saves the very complex simulation of

the protocol stack and the physical behavior of the

link. A well suited application area of this system is

in the simulation of swarms, multi-robot teams, and

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

316

formation driving. In these scenarios, control algo-

rithms can be tested and evaluated under the inﬂuence

of real communication link behavior (limitations in

medium access etc.) and also different communica-

tion protocols can be tested easily. Of course, also

some limitations exist. As the hardware nodes are lo-

cated quite close to each other, long distance com-

munications and the consequential channel behavior

cannot be simulated. Nevertheless, for detailed sim-

ulations of the interaction of communication proto-

cols, control engineering and the underlying system

in multi-robot teams and formation scenarios, the pre-

sented architecture is suites very well and allows an

easy and fast setup of signiﬁcant simulations.

5 CONCLUSIONS

In order to simulate the behavior of networked multi-

robot systems in general a model for the communica-

tion channel has to be to implemented and veriﬁed.

In most cases this is only possible with simpliﬁca-

tions and limitations and the simulated channel is not

representing a real communication channel anymore.

Therefore, the conclusions drawn from the simulation

of the tested algorithm might not be as meaningful as

desired.

The presented approach offers a possibility to re-

alize easily a meaningful simulation with real com-

munication hardware for network robotic scenarios.

It provides the exact behavior of the complete, com-

plex communication stack without any approximation

or simpliﬁcations. Thus, the behavior of multi-robot

algorithms can be directly investigated with all the

changes in the communication and data ﬂow between

the robots. In the combination with a simulator like

USARSim is is possible to simulate network robotic

systems with basic physics and real communication.

The setup of a hardware communication in the

loop simulation is much easier than the setup of a

meaningful communication channel simulation com-

bined with a multi-robot simulation. There are even

less uncertainties in the behavior of the system when

you later go to real hardware.

Therefore, it is very easy to test the behavior of

algorithms for typical applications of network control

systems like teleoperation of robots or robot forma-

tion driving with a real communication channel be-

fore going to the real hardware. Due to the standard-

ized interfaces which are used, such kind of setups

also allow for an easy evaluation of different type of

wireless communication systems like e.g. WLAN,

UMTS, HSDPA/HSUPA, Bluetooth, WiMax. Espe-

cially testing of swarm behavior is very meaningful,

because like in the real system naturally the nodes in

the communication hardware in the loop simulation

are very close to each other.

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