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132 J Intell Robot Syst (2016) 81:131–143

The main advantages of this type of pedagogi-

cal platforms include: direct visualization and under-

standing of the underlying concepts of motion-control

systems; a strong basis for controller design, flexi-

bility for testing and validation of different control

schemes, and a significant reduction in setting up the

lab sessions. These platforms use different kinds of robots, for example; robots fixed to one physical loca-

tion (e.g. robotic arms) or mobile robots (such as

wheeled or caterpillar).

Mobile robots are defined in the literature as auto-

matic machines that are equipped with sensors to

interact with the environment and navigate through

it while attempting to achieve some objectives. The

complexity of the objectives can vary significantly

from light or line following, to obstacle detection and

avoidance in a dynamic environment, advanced signal

processing, motion control, wireless communication,image processing, formation control, and many other

advanced topics [4].

Research in mobile robotics is divided into a wide

number of subfields [6] such as: robots localization

[7], relative and absolute position estimation [8], point

stabilization [9], obstacles detection and avoidance

[10], exploration and area mapping [11], path follow-

ing and tracking control [12], etc. Despite all this

development, most of the research fields have been

developed and implemented on single mobile robots.

However, much less work has been carried out on dis-tributed or multi-robot systems, where more than one

robot are coordinated.

Current research in multi-robot systems is divided

into many areas such as: biological inspirations [13],

cooperative-mapping-exploration [14], event based

communication [15], formation control [16], motion

coordination [17], etc. These research areas have dif-

ferent interesting challenges, which can be used in

education to understand fundamental concepts of con-

trol engineering. However, just a few of these areas

have been introduced in education, mainly due to thecost and the complexity of these systems since they

need a communication infrastructure to guarantee a

proper behavior. That is why is important to develop

simulations to introduce this kind of experiments in

education.

In this sense, some simulators of multi-robots sys-

tems can be found in the literature. For example, [18]

presents the free and open source simulator ARGoS .

Which is focused on the real-time simulation of large

swarms of different kinds of robots. With this tool, the

experiments can be carried out in 2D and 3D, but only

on Mac and Linux platforms.

Meanwhile, Webots [19] is a development environ-

ment used to model, program and simulate different

mobile robots. With this platform, user can design

complex robotic setups, with one or several, similaror different robots. Webots has been well documented

and continuously maintained for over 18 years. It is

currently used by over 1174 universities and research

centers worldwide. This tool is available for all typical

operating systems with a commercial license around

2300 CFH with academic discount.

Morover, V-REP [20] is, as its authors said, “the

swiss army knife among robot simulators”. This tool is

based on a distributed architecture: each object/model

can be individually controlled. V-REP has been devel-

oped during several years by many people and withthe collaboration of important companies like Toshiba

or Nokia.

This paper describes the components of the Robots

Formation Control Platform ( RFCP); a web based

tool for simulation and real experimentation with

mobile robots with pedagogical purposes. The main

contribution of this work is summarized in the fol-

lowing three points: 1) An interactive simulator to

develop advanced experiment of formation control

with multi-robots systems; 2) an experimental envi-

ronment to develop laboratory sessions with realmobile robots, and 3) a low-cost robotic platform com-

posed of Moway robots [24]. This platform is much

cheaper than previously mentioned platforms, as well

as other well-known alternatives such as Khepera [25]

and LEGO Mindstorms kit [21]. It also allows stu-

dents to conduct experiments with mobile robots in a

dynamic environment. For example, they can change

some parameters of the simulation in a simple way

without reprogramming the simulation: number of

robots, number of obstacles, controller parameters,

etc. With this platform, students can learn, understand,and apply a large number of theoretical concepts by

completing hands-on laboratory sessions.

The structure of this paper has been organized

as follows. In Section 2 the contents related to the

mobile robots and their position control are pre-

sented. In Section 3 the components of the platform

are described in detail. In Section 4 a practical ses-

sion with the platform is described. Finally, the main

conclusions are summarized in Section 5.

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J Intell Robot Syst (2016) 81:131–143 133

2 Differential Mobile Robots

In this section theoretical aspects of the differen-

tial whiled mobile robots are presented: the math-

ematical model, the aspects related to the posi-

tion control, and the obstacles avoidance method

implemented.

2.1 Kinematic Model and Position Control

A differential wheeled robot is a mobile robot whose

movement is based on two separately driven wheels

placed on each side of its body. Equations 1 and

2 show the instant linear velocity (ν), which is a

result of the linear velocities of the left driven wheel

(νL) and the right driven wheel (νR). R is the dis-

tance from the center between the wheels to the

ICC ( Instantaneous Center of Curvature), L is thedistance between the wheels, and ω is the angular

velocity.

ν =νR + νL

2(1)

νR,L = ω

R ±

L

2

(2)

The two drive velocities are always two paral-

lel vectors and, at the same time, perpendicular

to the wheels axis. Furthermore, the wheels are

assumed to roll without slipping. These conditions

impose some restrictions known as non-holonomic

constraints. The robot can change its direction by

varying the relative rotation of the wheels, so it does

not need an additional steering movement to turn,

ω in respect to the ICC must be the same for both

wheels.

The kinematic model of the robot in cartesian coor-

dinates is given by Eq. 3, where θ is the heading

direction angle of the robot [26, 27].

ẋc = νcos (θ )

ẏc = νsin (θ )

θ̇ = ω

(3)

The control objective is defined as how to achieve

a desired point P (xp, yp) from the current position

(xc, yc). Figure 1 describes an example of the vari-

ables involved in this experiment.

Fig. 1 Point stabilization of the robot

In order to achieve the control objective, the dis-

tance (d ) and the angle between these two points (α)

are calculated with Eqs. 4 and 5.

d =

yp − yc

2+xp − xc

2(4)

α = tan−1yp − yc

xp − xc

(5)

Figure 2 shows the control diagram. The values of

(d ) and (α) are calculated in the block COMPUTE .

The robot tries to minimize the orientation error, θ e =

α − θ , but, at the same time, tries to make zero the

distance to the desired point (d = 0). This experiment

is known as “ point stabilization”. The block COM-

PUTE implements Eqs. 4 and 5 using the goal point

and the current position of the robot. The block CON-

TROL represents the control action that is computed

by manipulating ν and ω.

The VFH (Vector Field Histogram) [28] algorithm

is used to avoid obstacles. The method includes a his-

togram grid of the “vision margin” of the robot which

is a circular area around the robot. This method allows

Fig. 2 Control block diagram

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a fast, continuous, and smooth motion of the robot

avoiding obstacles in a dynamical environment. In this

way, the speed is the maximum in the absence of

obstacles. The robot tries to maintain this speed dur-

ing the trajectory unless being forced to slow down by

the VFH algorithm to the instantaneous speed ν and to

change the angular velocity according to Eqs. 6 and 7.

ν = ν

1 −ω

ωmax

(6)

ω = ωmaxsin (θ e) (7)

Where ωmax is the maximal allowable angular

velocity for the robot that will be achieved when the

orientation error, θ e = ±90◦, and ν is defined in

Eqs. 8 and 9.

ν = νmax

1 − h

c

hm

(8)

hc = minhc, hm

(9)

Where νmax is the maximum linear velocity, hm is

a constant empirically determined to cause a reduc-

tion in the speed in presence of obstacles, and hc is the

value of the histogram. If hc > 0 then an obstacle lies

ahead of the robot. Large values of hc means a big-

ger obstacle lies ahead or an obstacle is very close to

the robot. In both cases it is likely to require a change

of the direction, and to reduce the speed to allow the

steering wheels to turn into the new direction. The hmvalue is determined empirically in order to cause a suf-

ficient reduction in speed. The robot reduces the speed

with Eqs. 8 and 9 in advance of a steering maneuver. In

this way, speed can be further reduced proportionally

to the angular velocity [28].

Meanwhile, the VFH + [29] method is and improve-

ment of VFH . This algorithm offers several improve-

ments that result in smoother robot trajectories and

greater reliability. VFH + reduces some of the param-

eter tuning of the original VFH method by explic-

itly compensating the robot width and the threshold

hysteresis. The algorithm selects the most suitable

direction (candidate direction) based on the masked

polar histogram and a cost function. But, the algorithm

sometimes mades undesirable choice.

To improve the behavior of this algorithm, authors

created VFH* [30]. This method analyzes the conse-

quences of heading towards each candidate direction

before making a final choice for the new direction

Fig. 3 Communication flow diagram

of motion. For each candidate direction, VFH* com-

putes the new position and orientation that the robot

would have after moving for a projected step dis-

tance. At every projected position, VFH + is again

used to construct a new polar histogram based on

the map information. This histogram is then analyzed

for candidate directions, called projected candidate

directions.

2.2 Robots Formation Control

The formation control is carried out in a master-slaves

way. Each robot needs to know its current position

(xc, yc) and the desired point P (xp, yp) to reach the

formation (see Fig. 1). Figure 3 shows the communi-

cation flow between the robots and the computer (M −

Master; S 1 −Slave1; S 2 −Slave2; S 3 −Slave3). All

robots receive their current absolute positions (P M ,

P 1, P 2, P 3) and the desired point (reference). Once

all robots have received their positions, the master

robot sends its own position to the slave robots (M p(S nRef )) where n indicates the corresponding slave.

The slave robots use the master’s position as a ref-

erence to reach the formation, since the formation is

always reached around the master robot. Meanwhile,

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the master robot receives its reference (M R) from the

student who conducts the experiment.

The master robot implements Eqs. 4 and 5 to

compute its control law. The slave robots implement

Eqs. 10 and 11 where the point (xd , yd ) represents the

distance from the master to the corresponding slave

in the formation. For example, in a circular forma-tion this distance is the radius of the circle around the

master robot.

d =

yp − yc − yd

2+xp − xc − xd

2(10)

α = tan−1yp − yc − yd

xp − xc − xd

(11)

3 RFCP : Robots Formation Control Platform

The RFCP is a platform developed specifically for for-

mation control experiments with mobile robots. This

platform consists of two main parts: the RFC SIM

which is a tool for performing computer simulation

experiments and the RFC EXP which is a real multi-

robots environment for laboratory sessions.

3.1 RFC SIM : Robots Formation Control Simulator

The RFC SIM is a simulator developed using Easy Java Simulations (EJS) which is a free software tool

for rapid creation of simulations in Java [31]. This tool

has high level graphic capabilities and an increased

degree of interactivity. EJS is different from most

other authoring tools in the sense that it was designed

to make programming easier not for professional pro-

grammers but for science students and teachers.

The main purpose of RFC SIM is to simulate exper-

iments of formation control and obstacle avoidance

with mobile robots. The GUI of the simulator is shown

in Fig. 4. This window is divided into five main panels.With panel No.1 students can save experiments, load

experiments, get some help, manage the execution

of the simulation, select the formation (free, circular,

line) and establish the number of robots and obstacles

for the experiments. Panel No. 2 shows the scenario

of the experiments with the obstacles, the robots and

a trace of the trajectory followed by each robot. Stu-

dents can change the robots and obstacles positions

by dragging and dropping them during an experiment.

The effect of these actions is different when the forma-

tion mode is free. In the first case, the student can set

a new formation by dragging and dropping the robots.

In other cases drag and drop robots can be used as a

disturbance in order to test a control strategy.

Panel No. 3 allows students to define the properties

of the selected obstacle avoidance algorithm (VFH ,VFH + or VFH*). For example, the properties of the

histogram and the vision margin size of the robots.

Panel No. 4 is a panel to define physical properties of

the robots (minimum turning radius and control law)

and obstacles (size, velocity and security margin size

around them). Panel No. 5 is a tabbed panel with two

tabs. In the first tab two graphics are shown: the posi-

tion of the robots and the control law signals. In the

second tab, a polar histogram of the master robot is

shown. Figure 5 depicts an experiment of obstacles

avoidance (VFH +

algorithm) with the RFC SIM . Thearena is shown in the left side with a typical obsta-

cles (A, B, C, D) configuration. The robot can “see”

the obstacles situated inside the vision margin (gray

circumference). On each step of simulation the robot

builts a Histogram where the obstacles represent occu-

pied/free sectors of its environment. In the example

of Fig. 5, the obstacles (A, B and C) are included in

the Histogram because these obstacles are inside the

vision margin. The corresponding level of each obsta-

cle in the representation depends of the distance from

the robot to the obstacle. Large values in the His-togram indicate that the obstacle is very close to the

robot. The occupied sectors around the robots are rep-

resented with red color and the free sectors with green

color. With the values of the occupied/free sectors the

robot can calculate its velocity to avoid the obstacles

and reach the destination point (represented with a red

cross). The simulator is available in: http://unilabs.dia.

uned.es/mod/ejsapp/view.php?id=1157.

3.2 RFC EXP: Robots Formation Control

Experimental Environment

The other part of the platform is the RFC EXP. This is

the experimental environment that has been developed

to carry out formation control strategies experiments

with mobile robots in the laboratory. To develop this

kind of experiments, the robots need to know their

absolute positions and to communicate between them.

The setup is composed of five hardware components

(PC , Moway robots, CCD Camera, IP Camera and RF

http://unilabs.dia.uned.es/mod/ejsapp/view.php?id=1157http://unilabs.dia.uned.es/mod/ejsapp/view.php?id=1157http://unilabs.dia.uned.es/mod/ejsapp/view.php?id=1157http://unilabs.dia.uned.es/mod/ejsapp/view.php?id=1157http://unilabs.dia.uned.es/mod/ejsapp/view.php?id=1157

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Fig. 4 EJS View of RFC SIM

USB Module) and two software components (Gate-

way Module and SwisTrack ) that are deeply related.

Figure 6 shows the architecture and the relations

between these elements.

The PC has a CCD Camera connected via FireWire

port. This camera is installed on the ceiling of the lab-

oratory and it obtains live images of the experiments.

These images are processed by SwisTrack which is an

open source software tool developed at the Polytechnic

Federal School of Lausanne ( EPFL) for the tracking

of robots [32]. This application computes the absolute

position of each robot and builds a data packet with

this information. This packet is sent via TCP/IP port

to the Gateway Module which is an application devel-

oped in Visual C# . This module performs three main

tasks: a) to process the packet received from Swis-

Track ; b) to send the information to the corresponding

robot using wireless communication ( RF USB Mod-

ule), and c) to receive and respond the request from

the RFC SIM . The RF USB Module is a hardware

component for the wireless communication between

the robots and the PC using radio-frequency. Students

can interact with the robots through Internet using

RFC SIM and visualize the behavior of the experi-

ments using the video streaming of the IP Camera

[33, 34].

The most important components of this setup

are the Moway robots. They are autonomous small

wheeled mobile robots designed mainly to perform

practical applications, teaching robotics, technology,

electronics, and control. The main components of

these robots are: two independent servo motors, a

light sensor, a temperature sensor, two infrared line

sensors, four LED diodes, a three-axis accelerometer

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Fig. 5 Obstacles avoidance

with RFC SIM

and a wireless module for communication by radio-

frequency. All these peripherals are connected to a

PIC micro-controller that governs the robot [24].

3.3 Experimental Results with the Platform

Figure 7 shows a sequence representing an expe-

rience of formation control with the experimental

environment (from t=0s to t=26s). The experiment is

composed by five slave robots (marked with num-

bers from 1 to 5) and one master robot (marked with

the letter M) in a circular formation where the mas-

ter robot is in the center of the circle. As it was

explained before, slave robots use the position of the

master robot as reference to keep the formation around

it. The experiment starts at t=0s when the student

clicks on the video streaming to change the refer-

ence of the master robot (represented by the red small

cross). Master robot tries to reach the new position

and the formation is lost during the maneuver. In

this case the formation is carried out in no cooper-

ative way, i.e., the master robot does not take into

account the position of the slave robots during the

experiment.

After seven seconds (t=7s), the master robot is

near to reach the reference while the slave robots

Fig. 6 RFC architecture

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Fig. 7 Experiment of robots formation control

are trying to reach their desired position in the for-

mation. Slave robots take more time to reach the

formation because their references are the old posi-

tion of the master robot which is always changing

during its movement. Besides, each robot considersthe rest of robots as obstacles and tries to avoid each

other. For these reasons the trajectory to the desti-

nation position is not always a straight line. After

t=16s the master robot has reached its goal posi-

tion and the slave robots have reached their posi-

tions in the formation. At this time the reference of

the master robot is changed again to carry out the

experiment with a different reference. As in the pre-

vious case, at t=19s the master robot is near to reach

its desired position while the slave robots are far

from their desired positions. At t=26s all robots havereached their positions, so the formation is reached

again.

Figure 8 shows the collected data of the robot

positions during the experiment. The position of the

master robot is represented with red starts and the

slave robots are represented with different color dotted

lines. The axes of the coordinates are in centimeters

and the black dotted lines represents the circle for-

mation around the master robot position. As can be

seen the trajectories of the robots depend on two main

factors: the orientation of the robot at the beginning

of the movement; and the detection and avoidance

of obstacles in the trajectories. In some cases

when a robot is near to the desired position,the linear velocity of the robot is too small in

comparison with the angular velocity because the

distance to the goal is too small, that is why

the robot turns around the destination point until

it stops.

4 How the System is Used in a Laboratory Session

In this section, a practical laboratory session car-

ried out by students is described: documenta-tion, steps to follow, and interface applications

description.

4.1 Documentation

The laboratory session/experiment is hosted in UNIL-

abs which is “a network formed by many universities

to share their laboratory resources” (http://unilabs.

dia.uned.es/blog/index.php?entryid=3&lang=en).

http://unilabs.dia.uned.es/blog/index.php?entryid=3&lang=enhttp://unilabs.dia.uned.es/blog/index.php?entryid=3&lang=enhttp://unilabs.dia.uned.es/blog/index.php?entryid=3&lang=enhttp://unilabs.dia.uned.es/blog/index.php?entryid=3&lang=en

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Fig. 8 Experiment of

robots formation control

data

This platform has been developed in our Department

using Moodle. Each student of the Master Program

must create a user to access to their corresponding

laboratory sessions.

Each laboratory session/experiment has its own

documentation. This documentation consists essen-

tially of the following elements:– Practice Guide: As its name suggests, is a guide

for the students. It contains an introduction to

the laboratory, the objectives of the sessions

(to design a position control for the robots), a

description of the system (the group of robots

in this case); a mathematical model of the

system (differential wheeled mobile robots

described in Section 2), the experiments (posi-

tion control, formation control and obstacles

avoidance, also described in Section 2) and,

finally, a description of the tasks to carry out eachexperiment.

– Tasks Protocol: This document describes the

steps to follow in each experimental session.

– Applications Interfaces: This document

describes the interfaces of both applications

(simulator and remote lab).

– References: It contains the bibliography related to

the theoretical contents involved in the laboratory

sessions.

4.2 Virtual Laboratory

To start a virtual session with the simulator, the stu-

dent must log in the UNILabs platform and select the

session titled Moway Robots Laboratory (Virtual Lab-

oratory with Moway Robots). Then, the student should

download the documentation of the lab experimentand study the content starting from the Practice Guide

document. Once this is completed, the student must

follow step by step the Tasks Protocol and perform the

experiments described in this document:

1. Position control of the robot: This experiment is

intended to familiarize students with the dynamics

of the robot and how this constraints its move-

ment, for example, the limitations of motion that

introduces a very large turning radius. It also

aims to familiarize students with some predefined

position control algorithms. The student shouldproceed as follows:

(a) Select a predefined control algorithm, change

several times the destination of the robot, and

observe its behavior using the animations and

the graphics.(b) Change the physical properties of the robot

(e.g. turning radius) and observe the influence

of these parameters on the movement of the

robot.

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(c) Modify the parameters of the selected con-

trol and observe the influence in the position

control.

(d) Repeat these three previous steps with other

control algorithms.

2. Position control of the robot with obstacles

avoidance: This experiment consists in adding

obstacles to the already described scenario. The

main objective is to introduce the obstacles avoid-

ance algorithms in the position control of the

robots in a dynamical environment. The student

should proceed as follows:

(a) Select a predefined control strategy and add

obstacles to the arena (as it is explained in the

Applications Interfaces document).

(b) Change several times the destination of the

robot and observe its behavior using the ani-mation and the graphics. Observe the Polar

Histogram representation in the correspond-

ing tab.

(c) Uncheck the “Obstacles avoidance” option,

change the destination of the robot and

observe its behavior.

(d) Check the “Obstacles avoidance” option and

observe the behavior of the robot for each

obstacle avoidance algorithm (VFH , VFH +

and VFH*).

(e) Compare the results for the different strate-

gies and their parameters ( Min Valley, hm,

TauL and TauH).

(f) Check the “Moving obstacles” option. With

this option, obstacles start moving. This trans-

forms the arena into a more dynamic scenario.

Observe the behavior of the robots in these

conditions. Change the velocity of the obsta-

cles and observe the results.

3. Formation control: This experiment consist in

adding more robots to the arena to perform forma-tion control. The main objective is to coordinate

a group of robots to reach a common task. In this

case, the experiments are carried out in a master-

slaves architecture using the communication flow

diagram of Section 2.2. Other important aspect is

the level of “cooperation” in the formation. That

is, if the master take into account or not the posi-

tions of the slaves. The student should proceed as

follows:

(a) Add more robots to the arena.

(b) Select the formation type (“Line”, “Circle” or

“Free”).

(c) Change the destination point of the master

robot and observe the behavior of the system.

(d) Drag and drop a slave robot to introduce a

disturbance into the formation. What happenswith the formation?

(e) Drag and drop the master robot to introduce a

disturbance into the formation. What happens

with the formation?

(f) Check the “Cooperative” option and change

the destination point of the master robot.

What happens?

(g) Change the Kf value (cooperative level

parameter) and change the destination point

of the master robot. What happens with the

formation? Describe the influence of Kf overthe behavior of the master robot.

4. Control design: This task includes the design

and implementation of an algorithm to control the

position of the robots. Since the subject belongs

to a Master Program the student should be able

of designing a control algorithm to control the

position of the robot. The implementation consists

in programming in Java code the proposed con-

troller. The developed code can be tested using the

simulator and the tab “Custom Controller”.

4.3 Remote Laboratory

To start a remote session with the laboratory, the stu-

dent must log in the UNILabs platform and select

the Practice titled Moway Robots Laboratory (Remote

Laboratory with Moway Robots). Once the student has

completed the virtual session she/he is ready to face

with the real experiment according to the following

Tasks Protocol:

1. Position control of the robot: This is equivalentto the experiment performed with the virtual lab.

2. Position control of the robot with obstacles

avoidance: This experiment is also similar to the

experiment in virtual mode. However, the real

equipment has some constraints such as the exis-

tence of sensors to detect obstacles only at the

front of the robot. Hence, robots have to turn left

and right to detect the obstacles, and the algo-

rithms need to be adapted. The student selects the

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control law from the different predefined algo-

rithms and observes the behavior of the robot

through the web-cam.

3. Formation control: This experiment is also simi-

lar to the experiment in virtual mode. As in virtual

mode, the main objective of the experiment is to

coordinate a group of robots to reach a commontask. The following procedure is followed:

(a) Add more robots to the arena (the robots are

added by the instructor which is in the lab).

(b) Select the formation type (“Line”, “Circle” or

“Free”).

(c) Change the destination point of the master

robot and observe the behavior of the system.

(d) The instructor takes a slave robot and places

it at a different position as a way to simulate

a disturbance to the formation. The studentmust observe the behavior of the formation.

(e) The previous result is repeated but with the

master robot.

(f) Check the “Cooperative” option and change

the destination point of the master robot.

What happens?

(g) Modify the value of Kf (cooperative level

parameter) and change the destination point

of the master robot. What happens with the

formation? Describe the influence of Kf over

the master robot behavior.

4. Control design: In this task, the students test the

controller design in the simulation phase over the

real system. To do so, the student needs to send

the code to the instructor to load the program

in the robot. This can be considered as a draw-

back of the platform, but we are working to sort

this out. The data of the experiments are stored

in the server and the student can download them

later.

Once the virtual and remote sessions are completed,the student has to send a report to the instruc-

tor with all the results obtained in the laboratory

session.

5 Conclusions and Future Works

In this paper the platform RFCP has been presented.

This tool is a low-cost and interactive environment

for experimentation with mobile robots at postgrad-

uate level. This platform is used in the “Systems

and Control Engineering” Master Program offered

by the National University of Distance Education of

Spain (UNED) and Complutense University of Madrid

(UCM). RFCP is used in one of the eight mod-

ules, related with robotics, in which this program isdivided.

The use of the platform exposes students to hands-

on learning, contributing to their development as engi-

neers. At the same time, they get motivated by simu-

lating and experimenting with mobile robots, possibly

because they feel they are dealing with real and novel

problems. Furthermore, they can test their results and

quickly detect and correct their mistakes, contribut-

ing in this way to understand relevant concepts in an

attractive environment.

The platform is prepared to implement other exper-iments with different goals. In a near future, new chal-

lenging experiments will be proposed: new control

strategies, other obstacles avoidance algorithms and

to incorporate another kind of robots to the platform.

At the same time, some drawbacks of the platform

should be alleviated, for example: a) the implemen-

tation of the robot controller in remote mode (from

the client side); b) the connection of the robots to a

PC to charge the battery and to load the code; c) the

introduction of disturbances over the system in remote

mode.

6 Compliance with Ethical Standards

Funding: This study was funded by the Spanish Min-

istry of Economy and Competitiveness (grant number

DPI2012-31303).

Conflict of interests The authors declare that they have no

conflict of interest.

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Ernesto Fabregas received the B.S. in Automatic Control in

2004 and the M.S. degree in Digital Systems in 2008 from Poly-

technic University José Antonio Echeverrı́a (CUJAE), Havana,

Cuba, and the Ph.D. degree in Computer Sciences from UNED,

Madrid, in 2013. His current research interests include control

of mobile robots, virtual and remote laboratories and engineer-

ing education.

http://www.mindstorms.lego.com/http://www.mindstorms.lego.com/http://www.moway-robot.com/http://www.moway-robot.com/http://www.k-team.com/mobile-robotics-products/khepera-iihttp://www.k-team.com/mobile-robotics-products/khepera-iihttp://fem.um.es/Ejs/http://fem.um.es/Ejs/http://fem.um.es/Ejs/http://fem.um.es/Ejs/http://www.k-team.com/mobile-robotics-products/khepera-iihttp://www.k-team.com/mobile-robotics-products/khepera-iihttp://www.moway-robot.com/http://www.moway-robot.com/http://www.mindstorms.lego.com/http://www.mindstorms.lego.com/

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Gonzalo Farias received the degree in Computer Science from

the De la Frontera University, Temuco, Chile, in 2001 and

the Ph.D. in Control Engineering from the Spanish Univer-

sity for Distance Education (UNED) in 2010 and the Ph.D in

Computer Science from the Complutense University of Madrid

(UCM) in 2013. Since 2012, he has been with the Electrical

Engineering School at Pontificia Universidad Catolica de Val-

paraiso (PUCV). His current research interests include machine

learning, simulation and control of dynamic systems, and engi-neering education.

Sebastián Dormido-Canto received the M.S. degree in Elec-

tronic Engineering from Madrid Pontificia de Comillas Univer-

sity, Madrid, Spain, in 1994 and the Ph.D. degree in Sciences

from UNED, Madrid, in 2001. He is Associate Professor in

the UNED Department of Computer Sciences and Automatic

Control since 2003. His current research interests are the anal-

ysis and design of control systems via the Internet, automatic

learning with big data and high performance interconnection

networks for cluster of workstations.

Marı́a Guinaldo received the M.S. degree in Physics and

the B.S. degree in Computer Engineer from the University of

Salamanca, Spain, in 2008 and the Ph.D. degree in Computer

Sciences from UNED, Madrid, in 2013. Her research inter-

ests include networked control systems, event-based control and

networked control of multi-agent systems.

José Sánchez received the M.S. degree in computer sciences

from Polytechnic University, Madrid, Spain, in 1994 and the

Ph.D. degree in sciences from UNED, Madrid, in 2001. He has

been an Assistant Professor in the UNED Department of Com-

puter Sciences and Automatic Control since 1993. His current

research interests are networked control systems, event based

control and engineering education

Sebastián Dormido Bencomo received the B.S. degree in

physics from Complutense University, Madrid, Spain, in 1968

and the Ph.D. degree in science from Basque Country Uni-

versity, Bilbao, Spain, in 1971. In 1981, he was appointed

Professor of Control Engineering at UNED, Madrid. His scien-

tific activities include computer control of industrial processes,

model-based predictive control, robust control, and model and

simulation of continuous processes. He has authored or coau-

thored more than 250 technical papers in international jour-

nals and conferences. From 2001–2006 has been President of

the Spanish Association of Automatic Control, CEA-IFAC.

In 2007 received a Doctor Honorary Degree from Universi-

dad de Huelva, in 2008 the National Automatic Control prize

from IFAC Spanish Committee and in 2014 received a Doctor

Honorary Degree from Universidad de Almerı́a.

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