Integrating ROS and Gazebo Tools with a Network Security Module

to Support Secure Autonomous Robot Coordination

Mattia Giovanni Spina, Stefano Gualtieri and Floriano De Rango

Department of Computer, Modeling, Electronics and Systems Engineering (DIMES), University of Calabria,

Via P. Bucci, Rende, Italy

Keywords: Robot Coordination, Security, ROS, Gazebo, Autonomous Robots.

Abstract: Multi-robots system coordination is an important aspect to consider when complex task needs to be

performed. Even if robots are becoming always more autonomous, the collective behaviour and coordination

strategy can improve the overall performance in terms of execution time increasing the robustness of the

mission. However, few works addressed the issue of the network security related to the coordination strategy

and the current modelling and simulations tools are not ready to model security aspects that can affect the task

execution and in some case can compromise the mission The following paper proposes the integration of

some additional module on well-known tools such as ROS and GAZEBO in order to extend the modelling

aspects also on emerging trends to support technicians to evaluate the coordination strategies also form the

security point of view.

1 INTRODUCTION

Robot coordination and multi-robot applications are

gaining a lot of interest in these last years. Involving

more robots in missions or complex tasks has been

shown to produce many benefits in terms of success

of the mission or in terms or reduction of the overall

task execution time. A lot of attention in literature has

been given in these years about the coordination

strategy combining explicit coordination among

robot or implicit coordination. However, in our

opinion, a too few attentions have been given to

security aspects related to the robot coordination.

Even if a lot of work has been done on SLAM

technique (Park and Lee, 2017) to improve the

localization and perception of a surrounding

environment in a robot and also if some protocol to

distribute among robots partial built maps of the

surrounding environment, no attention has been

focused on some possible threats can be arise when a

robot can behave maliciously or some attacks can be

performed to degrade or compromise the task

execution.

The main contributions of this paper are listed below:

1. Network Layer Design: this contribution is

related to the introduction of a network layer in

https://orcid.org/0000-0002-3882-1678

ROS where all communication paradigm

supported is a publish/subscribe that is an

application layer mechanism. In our case we

simulated a network layer adopting an

application layer paradigm. This has been led

out introducing the channel model, the

communication range and the routing layer to

build the robot topology on the basis of the

exchanged packets. The main faced issues have

been the mapping of network functionalities at

the application layer to simulate the network

services.

2. Security Feature Design: ROS and Gazebo do

not consider any network security features.

This means that it is possible to model only

some robot characterization such as movement

and map building but it is not possible to

consider possible security threats related to

robotic applications. In future situations where

multi-robot systems can be involved in

complex tasks, the network security in the

robot communications and data sharing can be

a key issue to face because some critical

operations could be compromised. This means

that considering the current state of the art for

the mentioned simulators, security is not

supported (Rivera et al., 2019) (Mukhandi et

Spina, M., Gualtieri, S. and De Rango, F.

Integrating ROS and Gazebo Tools with a Network Security Module to Support Secure Autonomous Robot Coordination.

DOI: 10.5220/0010566303690377

In Proceedings of the 11th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH 2021), pages 369-377

ISBN: 978-989-758-528-9

369

al., 2019). At this purpose, some basic security

features for supporting authentication, integrity

check and encryption have been introduced and

integrated in the simulation and modeling

framework. More details on the network

security aspects will be presented in the next

sections.

All the proposal has been implemented integrating

two simulators such as ROS (Robot Operating

Systems) and GAZEBO that works at application

layer. The paper is organized as follows: section 2

presents the work in literature related to robot

coordination for unknown area discovery and

recruiting tasks; section 3 presents the main tools

adopted in our proposal to simulate map building of

the unknown area and robot coordination; section 4

introduces all modules and robot models considered

in our framework; the communication protocols and

modules for the recruiting task and robot coordination

are described in section 5; some security features and

threats are presented in section 6 and finally

conclusions are summarized in the last section.

2 RELATED WORK

Coordination of multi-robot systems received much

attention in recent years due to its vast potential in real

world applications. Simple robots work together to

accomplish some tasks. However, the execution of

complex task sometimes needs to involve multiple

robots. In this last case, robot coordination become an

essential point to guarantee. To perform this objective

the communication among robots is a key element to

consider and also possible threats to the

communication should be accounted. Robots’

coordination strategies can be broadly divided into

two main categories: explicit coordination and

implicit coordination.

Explicit coordination refers to the direct exchange of

information between robots, which can be made in the

form of the unicast or broadcast of intentional

messages. This often requires a dedicated on-board

communication module.

Existing coordination methods are mainly based

on the use of explicit communication that allows the

accuracy of the exchange of information among the

robots’ swarm (De Rango et al.,2018), (Tropea et

al.,2019). Instead, implicit coordination is usually

associated with implicit communication, which

requires the explorative robots to perceive, model,

and reason others’ behavior. In this case, an

individual robot makes independent decisions on how

to behave, based on the information it gathers through

its own perception with others. When the robots use an

implicit communication to coordinate, although the

information obtained by the robots is not completely

reliable, and the stability, reliability, and fault tolerance

of the overall system can be improved (Palmieri et al.,

2019), (Palmieri et al., 2018). However, in this last

case, an increase in the execution task among robots

can be observed. In our case we are interested in hybrid

approach where robots applying SLAM can move and

perceive the environment independently through its

sensors, but it can also receive information about

neighbor robots about part of the map already built by

them in order to speed up the overall task of unknow

space discovery.

Main contributions in comparison with the state

of the art are related to the integration of multiple

well-known tools for the robots modeling and

simulations with some modules to account for the

energy consumption, network layer modeling for

supporting the topology discovery and for the security

features to apply in the communication to reinforce

the explicit coordination mechanism. These two

aspects are essential to model and simulate real

context where robots can move and where some

threats can be present that can compromise the overall

mission.

3 SIMULATION TOOLS

Different tool and technologies have been applied to

implement our simulation scenario of robots under

security threats and coordination strategies.

3.1 Robot Operating System (ROS)

Robot Operating Systems (ROS) (Gatesichapakorn et

al., 2019) is a framework for the design and

programming of robot. It can create a robot network

where many processes can be connected. Moreover,

it offers all functionalities to design a distributed

system providing also services typical of an operative

system (OS) such as: hardware abstraction, device

controller through drivers, process communication,

application management (package) and other

features. Processes inside ROS can be represented

through graph structure where nodes can send,

receive and route messages coming from other nodes.

Nodes can also be sensors and/or actuators. Some

basic elements of ROS are recalled in the following:

1. Roscore: it is the master node that provides the

names registration and the discovery service of

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370

the other nodes. It can also set the connections

among nodes. If this node is not instantiated, no

communication is allowed among nodes.

2. Nodes: These are the entities that can store data

or computing tasks. Every process that needs to

interact with other nodes inside the ROS

network needs to be instantiated as a node and

it should be connected to the master node

(Roscore). In our case, robots are represented

by nodes.

3. Messages: they are the structure that represent

the messages exchanged among nodes. ROS

presents different default messages. However,

it is possible customize new messages with

additional info.

4. Topic: When a node sends data, it needs to

publish data on a particular data structure called

topic. It is like a publish/subscribe paradigm

where nodes can exchange data publishing

their data and other nodes can receiver these

data if they subscribe on the same topic. This

paradigm allows a separation between data

generation and data consumption.

3.2 Gazebo

Gazebo is an open-source 3D robotic simulator (Raje

and Sumit, 2020). It integrates the dynamic physic

engine called ODE (Open Dynamic Engine) that is

written in C/C++. It is equipped with a rendering tool

in OpenGL and it provides code to support robots,

sensors and actuators simulator. It supports a high-

resolution realistic rendering of the environment

where robots can move including lights and shadows

in the image detected by cameras. It can model

sensors that can perceive the surrounding

environment such as laser sensors, cameras (with the

large angle view) and sensors such as Microsoft

Kinect. Gazebo is very useful for robotic modeling

applications allowing also complex and detailed

simulations. A well-designed simulator allows to

quickly test algorithms, to design robots, to execute

regression test and to train artificial intelligence

systems using realistic scenarios. Gazebo offers the

possibility to simulate with high precision and

efficiency multi-robot systems for complex indoor

and outdoor environments. It can be integrated with

ROS through the package Gazebo-ROS.

3.3 Slam-Gmapping: Navigation

It is a module able to simulate the robot’s movement

applying the Gmapping technique (Abdelrasoul et al.,

2016). This last one is a highly efficient Particle Filter

technique such as Rao-Blackwellized designed to re-

build a map on the basis of data received by specific

sensors such as Laser whose robot is equipped. These

filters have been recently introduced to face issues

such as SLAM (Simultaneous Localization and

Mapping) (Ibáñez et al., 2017). In this approach, each

particle maintains an individual environment map.

This specific technique has the objective to reduce the

uncertainty related to robot location and it is

optimized for long-range laser sensors.

SLAM represents a process that allows a robot to

move in an unknown environment building at run-

time an environment map localizing itself inside the

MAP. It applies well-known techniques such as

Kalman filter, Covariance Intersection and

GraphSLA. SLAM algorithms can be applied and

adapted on the basis of the available resources in

order to reach a targeted objective. SLAM can be

applied in many robotic applications involving UAV,

underwater rovers or home robots. The module

supporting SLAM can be very useful for our purpose

because we can model the robot movement avoiding

collision during the movement and focusing more on

other objectives such as security or coordination

strategies. SLAM will support the robot navigation

system supporting a robot in detecting its position in

the reference frame related to the map and to plan a

path toward a target position. A robot to move needs

of an environment representation building a map and

interpreting correctly all info included in this map

representation. Even if in many applications the robot

can move with pre-loaded maps, in our case, we

applied SLAM to build MAP at run-time on the basis

of data collected by sensors.

4 MODULES AND

COMPONENTS FOR THE

ROBOT ASSESMENT

In the following sub-sections all modules and tools

adopted to simulate robots, sensors and environment

where robots move will be briefly presented.

4.1 Turtlebot 3

It is considered in our evaluation the Turtlebot3 robot

such as presented in (ROS.org “About Turtlebot 3”).

It represents a low-cost robot with open-source

control software and based on ROS environment. It is

often used in the academic environment, in the

research field and for prototyping embedded

solutions. TurtleBot3 can be customized and it is

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371

possible to extend its basic functionality introducing

additional modules focusing on specific target and

actions. The basic features include three different

versions: Burger, Waffle and Waffle Pi. Each of these

versions present different physical and technical

characteristics. In our case we considered the first one

because on the basis of its characteristics it can

present a lower energy consumption prolonging more

the battery lifetime such as shown in Figure 1.

Figure 1: Three types of Turtlebot.

4.2 Modules

The robot has been designed considering a modular

approach. This means that each robot is composed by

a set of modules executing specific tasks. Each

module has been implemented in Python and a

conceptual scheme of all modules implemented are

presented in Figure 2 below.

Figure 2: Block diagram of modules implemented in ROS.

The channel module has been designed to simulate

the physical channel that allow the communication

among nodes. In our case we considered this channel

as a broadcast wireless channel able to simulate

collisions, transmission and propagation delay in the

data forwarding. Through this channel module it is

possible to monitor all packets that travel on the

network and this will be useful in the security analysis

that will be presented in the next sections.

The specific component included in the robot model

are now briefly introduced:

1. Explorer Component: It is the module that

manages the exploration task. It is an important

module that allows the exploration of unknown

or known spaces in an autonomous way. It

allows the implementation of exploration

strategy that can use local knowledge of cells to

explore or novel points where to move.

Moreover, this module is connected with all the

other components coordinating the other

modules.

2. Battery-Buffer Component: it is an internal

module for the management of the battery and

the message buffer. It is considered in our case

a simple battery discharge model that considers

the time as variable to reduce the energy. On the

other hand, the buffer is considered to store

video frame produced by cameras on robot

about the surrounding environment. In our case,

it is considered a simple model to save a number

of frames proportional to the travelled distance.

3. Security Manager Component: it is the

module designed to manage all security

features. It manages the authentication, the

cryptography to support the confidentiality in

the data forwarding and the key negotiation and

exchange. In our case we considered ephemeral

keys and this means that the adopted keys are

applied for a limited amount of time and then

they need to be re-generated and exchanged

again. This approach has been used to mitigate

the key leak issue.

4. Home Handler Component: this module

manages the charging stations for robots. It is

essential because it allows robots to come back

home when their resources are exhausting. After

coming back to some of deployed base stations,

they can re-charged and it is possible also to

download all frames produced and stored during

the exploration.

5. Protocol Component: this module allows the

neighbor discovery and the topology table

update. The neighbor discovery is useful to

understand in a given time which robots are

directly connected to a specific robot. The

topology table building and propagation is

important instead to build the overall topology.

This last one is essential when we need to

involve/recruit some robots in more complex

tasks. Through these two submodules is possible

to maintain in the time the robots’ topology

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under dynamic condition such as robot

movements.

6. Recruit Component: it is a key component

because it focused on the logic to recruit other

robots on the basis of the perceived environment

and on the basis of the task to be executed. It is

also related to the communication protocol that

can be implanted and it can affect the overall

performance of the coordination and

cooperation strategy.

7. Communication Component: it is related to

the communication protocol and to the

periodical data forwarding to let other robots

know about neighbors and already explored

maps. The communication strategy is based on

the communication protocol selected and it is

related to the module presented above. This

module used in a joint way with the protocol

module are useful to reduce the exploring task

because they try to reduce the overlapping in the

exploration among robots.

5 COMMUNICATION NETWORK

LAYER PROTOCOLS

In order to model the topology table building and

propagation, it has been designed a network layer

protocol based on the publish-subscribe paradigm.

This choice has been determined by the basic

programming module implemented in ROS tool.

Avoiding to violate this basic feature offered by the

tool, we implemented a network layer adopting this

novel paradigm but considering all physical

conditions in the data propagation such as channel

modules (such as explained in the previous section),

radio propagation range etc. For our purposed we

considered a modified and enhanced version of the

Link State routing protocol where some metrics such

as residual energy, buffer space and robot distance

has been considered to build the robot connectivity

graph. The link-state based topology has been

essential to offer the possibility to the robot to recruit

other robots in order to cooperatively explore a

specific area reducing the exploration task time. In

addition to the link-state protocol, it has been

considered also another simple approach to recruit

robots that is based on a progressive recruiting

request forwarding. This recruiting protocol has been

called expanding ring - recruiting request protocol

because it propagates the recruit request considering

an incremental hop in the propagation whereas the

target number of robots to be recruited is not reached.

5.1 Link State Routing Module

The routing protocol implemented in the routing

module is the link state (LS). It is a protocol that

supports a local periodical update to build the

neighbor table and an event driven topology update

forwarded in broadcast to all robots to build an overall

consistent topology. The Link State Update (LSU)

packet considered for our purpose brings some

information useful for the specific coordination task

such as robot coordinates (X,Y), residual energy,

timestamp, sequence number etc.

Table 1: Some protocol packets field in the LS protocol.

Some fields included in the recruiting protocol are

presented in table II.

The protocol uses Dijkstra to build the minimum

spanning tree among robots. The metric adopted is the

minimum hop count and the residual energy is

important to evaluate if the recruited robot has

enough energy to reach the target position where to

perform the task. The routing module is flexible and

it is possible to select the local broadcast for updating

the neighbor table and it is possible to establish also

the metric to build the minimum spanning tree. This

module is connected with the security module when

some protection mechanism is applied in the LSU or

HELLO packet (local broadcast) update.

In the following some fields included in the link-state

routing protocol are presented.

Table 2: Protocols packet fields in the recruiting protocol.

5.2 Expanding Ring: Recruiting

Request

This strategy consists in forwarding the recruit

request at the beginning setting the TTL=1. This

allows a propagation of the recruit request just on the

first ring and on the direct neighbors. If no robots are

available to be recruited, after the recruit request

timeout, a novel request is sent with TTL=2 and so

on. This will allow to reach farer robots using

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373

intermediate robots to forward the recruit requests.

Two possible recruiting requests are possible:

 Specific Recruit Request: a robot can recruit

another robot that, after accepting the request,

will move towards the position indicated by the

recruiter.

 General Recruit Request: a robot can recruit

another robot that is at the base station to

recharge. This request will activate a robot that

was inactive at the base station.

On the basis of the recruiting requests presented

above, two conditions have been considered to be

managed:

 The robot exhausts its resources coming back to

the base station. In this case the robot requests a

specific recruit requests by other active robots.

 Incremental recruit: every pre-fixed amount of

time a robot is resumed and it can come back to

scan the area in order to reduce the overall tack

of map building;

In the recruiting phase, a robot can be recruited if it

has enough resources to be recruited. In our case it is

considered the battery level to know in advance if, on

the basis of the position where the robot should go, it

has enough energy to go there. It is preferred the

recruit if a robot that is inactive on the base station in

order to involve it in the space exploration task.

Secondly, the robot will be involved also considering

the distance. This means that the robot nearest in

terms of base station will be selected. When a robot is

terminating its resources, it will go to recharge on the

base stations that is closer to the robot.

6 SECURITY EVALUATION IN

ROBOT COORDINATION

In this section different security threats scenarios will

be considered. In the first scenario a personification

attack is considered and then an authentication

procedure is proposed as countermeasure.

Then, a second scenario where a specific integrity

attack has been considered with the correspondent

mitigation countermeasure; the last scenario

considered an attack to the confidentiality.

Performance metrics considered for the comparison

between secure and not secure recruiting strategies

are the following: number of exchanged packets,

dropped packets and energy consumption.

Another parameter accounted in the performance

evaluation has been the cryptography algorithm

applied. In our case we considered elliptic curve

cryptography applying three different elliptic curves:

secp192r1, secp256k1, secp384r1 (Shaikh et al.,

2017), (De Rango et al.,2020).

6.1 Authentication Attack

In the first scenario, 2, 3 and 4 robots have been

considered in the simulation and not security features

have been considered. In the network a malicious

robot has been accounted and it informs all robots that

it already explored the MAP in order to disincentive

other robots to explore the unknown area. This attack

will determine that each robot will present an

incomplete MAP and the overall task will fail. Under

this attack robots think that the task is complete and

they terminate to explore the area. In Figure 3 and

Figure 4 it is possible to see the number of exchanged

packets under attack and under a legacy behavior. It

is possible to see as the exchanged packets are less

among robots under attack because they assume that

the overall task has been performed. However, this

reduction in the protocol and control overhead leads

to an incomplete MAP formation such as it is possible

to see in Figure 5.

Figure 3: Task execution time for increasing number of

robots in legacy conditions or under attack.

Figure 4: Number of transmitted and lost packets for

increasing robot number and under attack 1.

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In the following it is shown the number of sent

packets. It is shown in Figure 5 the map building

under attack condition or in absence of malicious

behavior. It is possible to see as on the left side, a

correct map building is observed where red, green ad

grey colors represent the scanned map by legacy

robots. On the contrary, on the right side it is possible

to see the map building considering the same

simulation time when an attack is performed. In the

right figure, the area with oblique lines represents the

unexplored areas. This testifies as an attack in this

situation can compromise the mission and the task.

Figure 5: Map building in a scenario with and without

attack.

6.2 Mitigation at Scenario 1 Attack

In this case, a considered countermeasure is the

application of authentication procedure. In particular,

the robot starts an authentication procedure before

accepting the recruiting request. In this case the

following steps will be performed:

 The RSA has been applied to negotiate the

symmetric keys considered to encrypt the recruit

requests and messages where info about map is

included.

 The message integrity is guaranteed signing the

message through ECC cryptography.

 It the symmetric keys are successfully

exchanged between two robots, this assures that

the key and robots are authenticated.

6.3 Integrity Attack

In the second scenario it is attacked the

communication module because a man in the middle

attack (MiM) is considered. In this case, the MiM

robot will try to modify the packet info erasing the

info included in the packets. This will determine the

sensible reduction of the robot cooperation

determining an independent behavior of each robot

that will not know the already discovered area by

other robots. This attack will produce a useless packet

exchange among robots that want to share their info

without obtaining the main task objective and an

increase in the task completion time will be observed.

In Figure 6 it is shown the map to be discovered by

each robot and in Figure 7 it is shown the task

execution time under attack or under a legacy robots’

behavior. The same type of attack can be applied on

other component or procedure such as the recruiting

phase and requests.

Figure 6: Map representation within each robot.

Figure 7: Task execution time for a scenario without attack

and another one (Attack 2) with security attack.

6.4 Mitigation at the Scenario 2 Attack

The countermeasure adopted in the second scenario is

the adoption of hash function in order to guarantee the

integrity. In particular, the SHA256 has been applied

to manage the message integrity.

6.5 Eavesdropping Attack

Such as explained in section 1, ROS does not include

in its features any security features for messages

created to offer integrity and confidentiality. In

particular, all topics in the publish/subscribe

architecture are public and all robots can see all

messages that fall in the communication range. This

kind of issue is well-known, in literature, as

“Eavesdropping Attack” and it is usually used as a

starting point for more dangerous attacks.

6.6 Mitigation at the Scenario 3 Attack

In order to avoid possible attack to the confidentiality

all robot communications have been encrypted using

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375

a block encryption such as AES-128 (Kousalya and

Kumar, 2019) using ephemeral symmetric keys such

as explained in section 4. Moreover, in order to

mitigate the reflection attack, we managed a couple

of keys to manage encryption and decryption for

ingoing and outgoing traffic. It is possible to see in as

the ROS system, without our extension allows robots

to see all info exchanged in the packets. It is possible

to see as in the considered system the cryptographic

approach is effective hiding all info and providing

confidentiality property and complicating the attacker

work. In the following it is shown as the ECC curve

selected in the proposed approach to digitally sign the

packet can affect the execution time.

Figure 8: Task execution time under different ECC curves.

7 CONCLUSIONS

The following paper proposed to consider the security

aspects in the modeling and performance evaluation

of multi-robots systems. The coordination strategy

can be degraded or compromised by some possible

threats and it is important to include security features

in the communication protocols and on-board to

robots in order to protect them by possible cyber-

attacks. Current simulation tools such as ROS and

GAZEBO do not include in the basic features

modules able to consider security aspects and how

security aspects affect some constrained resources

such as battery and communication channel. Some

modules have been integrated in ROS and GAZEBO

to extend the modeling aspects to security. Some

mechanisms to support authentication, integrity and

encryption have been implemented. Moreover, some

security attacks have been applied to show how

mission or task can be compromised. Security

features have been introduced to mitigate these

attacks and performance evaluation has been

evaluated in a legacy or under attack scenario.

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