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ARTIGO CIENTFICO
Skill memory in biped locomotion (2. Parte)
A robotic platform for edutainment activities
in a pediatric hospital
AUTOMAO E CONTROLO
Automatizao de sistemas de bombagem
DOSSIER SOBRE INDSTRIA QUMICA
Qumica, a indstria com boa exportao
Reduzir o risco de transbordo
ESPECIAL SOBRE IMPRESSO 3D A primeira impressora 3D Made in Portugal
Imprima-Se em 3D
Utilizao das tecnologias de fabrico aditivo
no desenvolvimento de sapatos para pessoas
com paralisia cerebral
Contribuio para o desenvolvimento da Impresso 3D
CASE STUDY
Lubrigupo: Signum Oil Analysis: o poder de prever
WEGeuro: Gama de motores WEG W22 Super
Premium reduz perdas em 40%
Schaeffler: Guias lineares 4.0
Weidmller: Blocos de equalizao potencial JB 2550
e EBB 2550/16
RUTRONIK: A Indstria 4.0 tem de provar que vale
o investimento
ENTREVISTA
a nossa recente Cer tificao Ambiental
um fator impor tante para a nossa competitividade
no mercado, Snia Silva, WEGeuro
fornecedor lder em solues e produtos vocacionados
para a produtividade, Armando Mainsel, Europneumaq
Roadshowda Endress+Hauser em Portugal,
Paulo Loureiro
O mercado, devido crise, ficou muito mais exigente,
Jos Meireles, M&M Engenharia
100
ISSN 0874-9019
9 770874 901000
nmero 100 | 3. trimestre de 2015 | Portugal 9.50
PUB
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FICHATCNICA.SUMRIO
1
robtica
FICHA TCNICA
robtica 100
3.oTrimestre de 2015
Diretor
J. Norberto Pires, Departamento de Engenharia Mecnica,
Universidade de Coimbra [email protected]
Diretor-Adjunto
Adriano A. Santos, Departamento de Engenharia Mecnica,
Instituto Politcnico do Porto [email protected]
Conselho Editorial
A. Loureiro, DEM UC; A. Traa de Almeida, DEE ISR UC;
C. Couto, DEI U. Minho; J. Dias, DEE ISR UC;
J.M. Rosrio, UNICAMP; J. S da Costa, DEM IST;
J. Tenreiro Machado, DEE ISEP; L. Baptista, E. Natica, Lisboa;
L. Camarinha Matos, CRI UNINOVA; M. Crisstomo, DEE ISR UC;
P. Lima, DEE ISR IST; V. Santos, DEM U. Aveiro
Corpo Editorial
Coordenador Editorial:Ricardo S e Silva
Tel.: +351 225 899 628 [email protected]
Diretor Comercial:Jlio Almeida
Tel.: +351 225 899 626 [email protected]
Chefe de Redao:Helena Paulino
Tel.: +351 220 933 964 [email protected]
Design
Luciano Carvalho [email protected]
Webdesign
Ana Pereira [email protected]
Assinaturas
Tel.: +351 220 104 872
[email protected] www.engebook.com
Colaborao Redatorial
J. Norberto Pires, Adriano A. Santos, J. Andre, C. Santos,
L. Costa, Joo Messias, Rodrigo Ventura, Pedro Lima,
Joo Sequeira, Paulo Alvito, Carlos Marques, Paulo Carrio,
Frederico Lucas, Paula Domingues, Miguel Malheiro,
Lus Arajo, Francisco Mendes, Amrico Costa,
Jorge Lino Alves, Lgia Lopes, Ana Dulce Meneses,
Carlos Alberto Costa, Rosrio Machado,
Ricardo S e Silva e Helena Paulino
Redao, Edio e Administrao
CIE - Comunicao e Imprensa Especializada, Lda.
Grupo Publindstria
Tel.: +351 225 899 626/8 Fax: +351 225 899 629
[email protected] www.cie-comunicacao.pt
Propriedade
Publindstria - Produo de Comunicao Lda.
Empresa Jornalstica Reg. n. 213 163
NIPC: 501777288
Praa da Corujeira, 38 Apartado 3825
4300-144 Porto
Tel.: +351 225 899 620 Fax: +351 225 899 629
[email protected] www.publindustria.pt
Publicao Peridica
Registo n. 113164
Depsito Legal n.o372907/14
ISSN: 0874-9019 ISSN: 1647-9831
Periodicidade: trimestral
Tiragem: 5000 exemplares
INPI: 365794
Impresso e Acabamento
Grficas Anduria
Avda. de San Xon, 32
36995 POIO (Pontevedra)
Os trabalhos assinados so da
exclusiva responsabilidade dos seus autores.
SUMRIO
da mesa do diretor
2 O mundo virado do avesso
artigo cientfico
4 Skill memory in biped locomotion (2. Parte)
10 A robotic platorm or edutainment activities in a pediatric hospital
empreender e inovar
16 Sentido da vida
automao e controlo
18 Automatizacao de sistemas de bombagem
eletrnica industrial
22 Fabrico de circuitos em PCI
instrumentao
28 Vlvulas de segurana e alvio
30 notcias da indstria
48 dossier sobre indstria qumica
49 Qumica, a indstria com boa exportao
52 Reduzir o risco de transbordo
56 especial sobre impresso 3D
57 A primeira impressora 3D Made in Portugal60 Imprima-Se em 3D
62 Utilizao das tecnologias de abrico aditivo no desenvolvimento de sapatos para pessoas com paralisia cerebral
66 Contribuio para o desenvolvimento da Impresso 3D
informao tcnico-comercial
68 igus: Transerncia de dados mais segura para aplicaes mveis na Indstria 4.0
70 Bucim Ex da Weidmller
72 Omron: Aranow Packaging Machinery
74 Zeben: DataloggersMSR: pequenos ormatos multiuncionais
76 Schaeffler Iberia: 7,6 milhes de euros de indemnizao pela distribuio de rolamentos FAG alsificados
78 Rittal apresenta nova gerao de ar-condicionados Blue e+
80 LusoMatrix: Unictron antenas CHIP
82 WEGeuro: Eficincia energtica em silos de armazenagem de gros
84 M&M Engenharia Industrial: Esquemas em metade do tempo
86 EGITRON/MECMESIN Controle a qualidade dos materiais da sua embalagem
88 JABA-TRANSLATIONS: criao e traduo de documentao tcnica
case study
90 Lubrigrupo: Signum Oil Analysis: o poder de prever
94 WEGeuro: Gama de motores WEG W22 Super Premium reduz perdas em 40%
96 Schaeffler Iberia: Guias lineares 4.0
98 Weidmller: Blocos de equalizao potencial JB 2550 e EBB 2550/16
100 RUTRONIK: A Indstria 4.0 tem de provar que vale o investimento
entrevista
102 a nossa recente Certificao Ambiental um fator importante para a nossa competitividade no mercado,Snia Silva, WEGeuro
104 fornecedor lder em solues e produtos vocacionados para a produtividade, Armando Mainsel, Europneumaq
106 Roadshowda Endress+Hauser em Portugal
110 o mercado, devido crise, ficou muito mais exigente, Jos Meireles, M&M Engenharia
reportagem
112 SEW-EURODRIVE PORTUGAL comemora 25 anos
114 bibliografia
116 produtos e tecnologias
138 calendrio de eventos
140 eventos e formao
144 links
www.robotica.pt
Aceda ao linkatravsdeste QR code.
/revistarobotica
Apoio capaMore Perormance
Simplified.u-remote.
Weidmller Sistemas de Interface, S.A.Tel.: +351 214 459 191 Fax: +351 214 455 [email protected] www.weidmuller.pt
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obtca
AMESA
E
O mundo virado do avesso
J. Norberto Pires
Pro. da Universidade de Coimbra
Olho para a imagem daquele beb e
digo peremptrio que poderia ser meu
filho; alis, mesmo. Nele tudo me to
prximo. Os sapatos, penso que a minhafilha mais nova tem uns iguais, as calas,
o ormato da cabea... a maneira como
est deitado, como que a dormir mais
uma vez, a minha filha dorme assim, co-
loca os braos nesta posio e aunda a
cabea no travesseiro numa imagem
que tem tudo para ser serena. E , na ver-
dade, terrivelmente serena. Nunca vou
esquecer esta imagem.
Olho para os comboios de Budapes-
te cheios de gente desesperada, marca-da como se osse dierente, que nasceu
do lado "errado" do mundo, cheia de vida
e sem esperana, a correr reneticamente
procura de um osis de paz e de espe-
rana. Leio que so interrompidos na sua
viagem para a Alemanha e para a ustria
para serem levados a campos desenha-
dos para pessoas "diferentes" como eles.
S os iguais seguem a sua vida a cami-
nho de casa, e de um destino que para
os outros seria uma oportunidade de
sobrevivncia. Imagino outros comboios,noutros tempos que, em sentido inver-
so, traziam gente considerada "diferente"
para stios sem esperana, sem vida e
onde o destino era a morte.
Olho para os dirigentes europeus e
no vejo urgncia. Quando estava em
causa o dinheiro as reunies eram mar-
cadas em menos de 14 horas, mas neste
caso estando em causa vidas a urgncia
relativa e mede-se em semanas. Ouo
as suas palavras e pasmo de vergonha.Estamos perante uma "praga" de gen-
te dierente que no problema nosso,
mas antes da Alemanha. Ouo e leio isto
de pessoas que dirigem pases e no tm
a mnima vergonha, nem sobressalto hu-
mano e cvico, de o dizerem alto e em
pblico.No deixa de ser curioso ouvir a chan-
celer alem, com toda a razo, a apelar a
uma resposta coordenada da Europa,
unida em torno de valores superiores, de-
pois da campanha de desunio e desin-
teresse que promoveu para o problema
grego. Agora percebe as consequncias
de to mesquinha e irrefletida atuao. A
Europa escolheu a autodestruio e deu
voz aos nacionalismos mais primrios.
Agora com a crise dos reugiados pura esimplesmente no tem resposta, porque
no existe como UNIO.
No tenho nenhuma esperana que
o beb que podia ser meu filho, os com-
boios de Budapeste ou a vergonha das
declaraes de certos lderes europeus
alterem seja l o que or. O mundo est
numa encruzilhada terrvel. Desaparece-
ram os valores e tudo mais importante
do que as pessoas, os seus sonhos e as
suas vidas. A prova que na Europa, antes
uma esperana de um mundo melhor, selevantam muros, se acicatam antasmas
e se adiam respostas. No importante,
pois no tem a ver, aparentemente, com
dinheiro. Lamento que tenhamos, de
novo, chegado aqui.
Nota final: observo os muitos mi-
lhes de euros que estamos disponveis
para gastar em pesquisa espacial, por
exemplo, para observar e compreender
novos mundos, e fico a pensar como no
sabemos nada do que se passa no nossoplaneta, nomeadamente com as pessoas
que c vivem. E fico a pensar na razo de
tudo isso.
"O mundo est numaencruzilhada terrvel.
Desapareceram os valorese tudo mais importante
do que as pessoas, os seussonhos e as suas vidas. A
prova que na Europa,antes uma esperana de um
mundo melhor, se levantammuros, se acicatamfantasmas e se adiam
respostas (...) observo osmuitos milhes de euros que
estamos disponveis paragastar em pesquisa espacial,por exemplo, para observar
e compreender novosmundos, e fico a pensar
como no sabemos nadado que se passa no nossoplaneta, nomeadamente
com as pessoas que cvivem. E fico a pensar na
razo de tudo isso."
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J.Andre1,C.Santos2,L.Costa3
1DepartmentofIndustrialElectronics,UniversityofMinho,[email protected]
2DepartmentofIndustrialElectronics,UniversityofMinho,[email protected]
3DepartmentofProductionandSystem,UniversityofMinho,[email protected]
Abstract Robots must be able to adapt their motor behavior to
unexpected situations in order to saely move among humans.
A necessary step is to be able to predict ailures, which result in
behavior abnormalities and may cause irrecoverable damage to
the robot and its surroundings, i.e. humans. In this paper we build
a predictive model o sensor traces that enables early ailure de-
tection by means o a skill memory. Specifically, we propose an
architecture based on a biped locomotion solution with impro-
ved robustness due to sensory eedback, and extend the concepto Associative Skill Memories (ASM) to periodic movements by
introducing several mechanisms into the training workflow, such
as linear interpolation and regression into a Dynamical Motion
Primitive (DMP) system such that representation becomes time
invariant and easily parameterizable. The ailure detection mecha-
nism applies statistical tests to determine the optimal operating
conditions. Both training and ailure testing were conducted on
a DARwIn-OP inside a simulation environment to assess and va-
lidate the ailure detection system proposed. Results show that
the system perormance in terms o the compromise between
sensitivity and specificity is similar with and without the proposed
mechanism, while achieving a significant data size reduction dueto the periodic approach taken.
Keywords Reinorcement learning Bio-inspired Skill Memory
6. FAILURE DETECTION
In order to properly take advantage o the inormation stored
into the ASM, we propose a system that monitors continuously
the execution o a motor skill (in this case biped locomotion)
and looks or deviations that could evolve into movement ailu-
res. The ailure detection protocol we introduce in this work was
inspired by Pastor et al. [20], but utilizes a more refined statisti-cal analysis in order to achieve the best results possible. Once
again we anchor the whole process on the phase values at any
given time. At each instant n o the simulation, and
, reconstructed rom the trained DMPs, provide the
ASM values or the correspondent phase (n), upon which a
statistical z-test is perormed. Thus, a tolerance interval is esta-
blished or each sensor according to:
(16)
where ytrial
(n) is the sensor data o the current simulation; (n)
is the phase at the current instant n in this trial,z =2.57 or a
confidence level o 99%, and and represent the
mean and standard deviation values stored into the ASM. I the
condition in equation (16) is satisfied, then the null hypothesis
o being a successul trial is not rejected and the sensor data is
assumed to be in conormity with the expected values - there
are no signs o ailure conditions. I, on the other hand, ytrial
(n)
is out o the confidence bounds established in (16), then the-
re is a high probability o ailure occurring, or that movement
objective is not achieved at the end o the trial. Whether or not
the current trial is flagged as ailure depends on the thresholds
or ailure detection: the minimum number o sensors M andthe minimum number o consecutive instants ailingN. Simply
put, i the system detects at leastMsensors ailing orNinstants
consecutively, then it is predicted that, based on the previous
experiences stored into the ASM, task execution will ail.
6.1. Detection Accuracy
Failure detection was interpreted as a simple two-class
classification problem, characterized by the sensitivity, conside-
red to be the probability o detecting a ailure on unsuccess-
ul trials and, similarly, by a specificityvalue, the probability o
rejecting ailures on successul trials. Conclusions about the per-
ormance o the ailure detection algorithm were based on adetection scorecomputed rom sensitivity and specificity values:
(17)
which can be interpreted as inversely proportional to the distan-
ce to the optimal operation point o maximal (100%) sensitivity
and specificity - a higher detection score implies better peror-
mance when detecting ailure conditions.
Figure 4.ROBOTIS DARwIn-OP humanoid robot in Webots.
6.2. Optimal Parameterization
MandN, thresholds or the number o sensors and number o
consecutive time steps, respectively, have a significant impact
on the perormance o the ailure detection system. With no a
prioriknowledge or practical know-how about how the sensor
readings vary, it is hard to estimate the optimal values or these
Skill memory in biped locomotionUsing perceptual informationto predict task outcome2. Parte
robtica100,3.oT
rimestred
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parameters. They have opposite impact on detection sensitivityand specificity, and as such optimal parametrization oMandN
values was achieved by plotting the ROC curve.
7. RESULTS AND DI SCUSSION
The robot used in the simulations is the DARwIn-OP by ROBOTIS
[14, 26], a small open-platorm humanoid with 20 DoF (6+6 in
both legs), using digital position controlled servos, measuring
45.5 cm and weighting 2.9 kg. The robots body is equipped with
a 3-axis gyro, a 3-axis accelerometer and each oot is equipped
with our orce sensing resistors (FSR) distributed through the
our corners. A simulated model o the robot is used in the 7.1
Webots simulation sotware, using the ODE physics simulator,
and can be seen in Figure 4.The system is integrated conside-
ring the Euler method with 8 ms fixed integration step, resulting
in a sampling rate o 125Hz. At each sensorial cycle, sensory in-
ormation is acquired. In order to replicate real world behavior in
the simulation sotware, noise magnitude was set at 5% in all ro-
bot sensors (oot orce sensors, gyroscope and accelerometer).
On the other hand, white Gaussian noise with an amplitude o
1.5% was generated and added to the joint positions computed
through the CPG dynamics
7.1. Feedback Mechanism
Locomotion is achieved in flat ground, with and without the
proposed eedback mechanism. However, an increase in loco-
motion quality is observed when compared to open-loop dy-
namics o CPG locomotion. In act, there is a 54.9% decrease in
the standard deviation in stride duration, as seen in Table 3. Inaddition, when comparing both types o locomotion, there is a
decrease in the average trial cost and in the number o ailure
trials. There is also a decrease in swing time when compared to
stance duration on a gait cycle, rom 42.85% to 34.44%.
Results o locomotion with and without eedback are sho-
wn in Figure 5, regarding right leg movement. Specifically, top
panel shows GRFright
, middle panel the phase right
, and bottom
panel the ankle pitch motion right,j
. Stance and swing phase
are highlighted in all panels. Oscillator phase is mainly modified
by the local sensory eedback during the stance phase, resulting
in steady walking. At the end o the stance phase the leg conti-
nues to bear a load, thus a phase delay is introduced which pro-vides enough time or the opposite leg to enter the stance pha-
se. As soon as the opposite leg begins to support the body, the
load on the right leg decreases accordingly. Meanwhile right leg
oscillator phase advances and the eedback effect decreases.
Thus, the right leg enters the swing phase. This illustrates the
behavior o the eedback loop implemented: the stance-swing
transition is delayed while the oot sensors are measuring non-
zero values. Besides, as discussed in [17], adaptation o the gait
depends on the body properties in a quantitative manner.
Figure 6 depicts the Centre o Mass (CoM) trajectory or
several noise magnitudes. It is notorious that open-loop CPGlocomotion is much less stable and more prone to deviation
rom the desired path. Besides, CPGs with sensory eedback
always travel more distance. Even when considering unrealistic
noiseless locomotion in a simulation environment, open-loop
CPGs have a tendency to deviate to the let. This deviation is
Figure 5.Simulation results o locomotion in the right leg movement with () and without () eedback, during the first 4 seconds o simulation. Top panel: gait dia-
gram (right and let leg), GRFright
and GRFleft
, respectively. Middle panel: phase right
. Bottom panel: ankle pitch motionright,AnklePitch
. The stance (grey areas) and swing
phase are highlighted in all panels.
Figure 6.Centre o Mass (CoM) trajectory with (-) and without (-) sensory eedback, or noise magnitude o 0, 0.5, 1.0 and 1.5% respectively. The noise seed used was the
same in all o these situations, in order to keep comparisons as realistic as possible.
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significantly attenuated with the implemented sensory eedba-
ck. With noise levels o 1.5%, open-loop CPG dynamics are no
longer able to achieve stable orward movement, instead devia-
ting rom the path as soon as the simulation starts.
Table 3.Mean and standard deviation o Step duration, during T = 1000 trials with
1.5% random noise at joint-level.
In this situation, eedback locomotion eventually alls as well,
however is able to cover more ground distance than open-loop
CPGs. Under the assumption that more regular and stable loco-
motion leads to less variance in step duration, we conclude that
the inclusion o eedback has improved locomotion quality by
providing a more regular gait. Additionally, as described in this
work, this type o phase eedback is useul as an anchoring pointo the data normalization process, and can be used as reerence
when perorming ailure check.
7.2. Failure Detection Parameterization
The ASM prediction model proposed implements a strai-
ghtorward ailure detection system by treating detection as
a two-class classification problem, in terms o sensitivity and
specificity. M and N, thresholds or the number o sensors
and number o consecutive time steps, respectively, have a
significant impact on the overall perormance o this ailure
detection system. However, there is no a priori knowledge or
practical know-how about how the sensor readings vary, andthus it is hard to estimate the optimal values or these parame-
ters. In order to find out the best possible parameter values, the
optimal operating conditions are achieved by assessing system
perormance o the system using ROC analysis.
SeveralM/Ncombinations were tested in both cases (with
and without data normalization) ,which resulted in the ROC cur-
ves presented in Figure 7.
The minimal Euclidean distance to the optimal operating
point (0,1), associated with maximal (100%) sensitivity and
specificity values, was used as evaluation criterion - the para-
meter pairing closest to this point achieves best perormanceand was used throughout this paper. Best perormance is thus
accomplished with (M,N) = (12, 2) beore normalization and
(M,N) = (22, 7) with normalization. It is noteworthy to observe
that ater normalization, a larger number o sensors ailing or a
greater number o consecutive instants is necessary to achieve
detection reliability. This may be caused by the act that ater
normalization ASM reerence values become a more generic
representation o the movement, and local deviations rom the
nominal values occur more requently.
7.3. ASM training and testing
Figure 8 depicts a subset o the 32 recorded signals used in the
ASM training phase. Unsuccessul trials (a total o 436 in 500) are
depicted in red and success ul ones (64 in 500) in green. The
weighted mean (black dashed line) and the weighted stan-
dard deviation (black solid line), beore normalization, are com-
puted rom the successul trials. On the remaining training runs,
56;60 successul trials and 444;440 unsuccessul trials composed
the training set. As previously explained, in the testing phase the
other 500 trials containing 55;63;59 successul and 445;437;441
unsuccessul trials were used.During the testing phase, sensor data is being monitored
online. DMP reconstruction o the memory data occurs at the
beginning o the trial, or each sensor. At each simulation time
step, the oscillator phase is used as reerence in order to pin-
point the current stage o the movement (i), and the corres-
pondent and or phaseivalue is extracted
rom the reconstructed DMP. Then the ailure detection z-test is
perormed using and .
This process is illustrated in Figure 9, where the data rom 3
different sensors (gyroscope CoM angle xx
, Let Foot Back Let
orce sensor GRFLFBLand the CPG oscillator phase i) is plot-ted or a successul and unsuccessul trial, during a small part
o the simulation and including two complete gait cycles. The
Figure 7.ROC curves or ailure detection beore (let) and ater (right) data nor-
malization (plotted values:N2{1, 2, 3, 4, 5} andM2{6, 8, 10, 12, 14, 16} beore
normalization andN2{3, 4, 5, 6, 7} andM2{16, 18, 20, 22, 24} ater normalization);
the optimal operation point in each case is the closest to (0,1).
Figure 8. Sensor traces recorded during 1000 simulation trials, or sensors #1-3 (gyroscope readings o CoM angle ), #11 (Let Foot Front Let Force sensor),
#28 (right,KneePitch), during the first 1.6s o the simulation trials. Unsuccessul trials are depicted in red and successul ones in green, with the weighted mean drawn as a
black dashed line and the weighted standard deviation as a black solid line.
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artigocientfico
7
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boundaries between movement periods are identifiable by
the values o right
. The light green shaded areas represent the
confidence intervals provided by the ASM trained with the train
subset. Deviation rom these intervals are easily perceptible in
the case o the unsuccessul trial (which resulted in a cost o
-0.2434 - no all occurred) but only occur momentarily in the
successul case (with a cost o -0.4295). Failure was only detec-
ted in the unsuccessul trial, as expected.
Table 4. Statistical detection results averaged over 3 training and testing runs,
beore and ater data normalization.
7.4. Detection Accuracy
A detection scorecomputed rom sensitivity and specificity va-
lues as given by (17) was computed to draw conclusions aboutthe perormance o the ailure detection algorithm. Similarly to
ASM training, ailure detection was conducted three times on
different Gtest
trial subsets.
From the results presented in Table 4, it is possible to veriy
that ASM normalization had little to no impact on ailure detec-
tion perormance, which is in line with the delineated objectives
or this work. Theoretically, normalization should provide no in-
crease in algorithm perormance, as its advantages are mainly
related to data representation and flexibility. Even so, detection
results improved slightly ater data normalization (higher de-
tection score). Despite achieving high sensitivity and specificity
values in both cases, the presence o alse positive and alse ne-
gative detections might be explained by ailure trials that have
cost values close to the threshold - a greater distinction betwe-
en success and ailure might be needed. Alternative reasons
might also include a deficient selection o sensor data. A useulmetric in these situations is the alse negative and alse positive
rates, which are computed as 1 sensitivy and 1 specificity
respectively, and quantiy the probability o occurrence o a al-
se negative/positive.
7.5. Prediction Interval
One o the main eatures o a properly trained ASM is allow
ailure detection ahead o time during uture task executions.
Detection o ailure conditions while executing a task occurs
the moment several perceived sensor signals deviate rom the
predicted ones. The prediction advance in the specific case orobot all during a trial is here quantified.
Figure 10 presents the whisker diagram drawn rom the pre-
diction interval tadvance
= tfailure
tdetection
values. Failures were
detected on average 3.63 seconds in advance without norma-
lization, although a decrease to 1.90 seconds was observed a-
ter normalization. This can partly be explained by two actors:
firstly, the value o N increased significantly with normalization
- deviation rom nominal values must occur or a larger interval
beore a ailure is detected, which leads to small deviations that
cause ailures without normalization being ignored; secondly,
as previously mentioned, the normalized ASM also takes into
account inter-step/period variations and thus leads to broaderconfidence intervals, and consequently a less specific sensor
memory. An example o a sensor trace in a successul trial is pre-
sented in Figure 9. The one-tailed t-test shows that we can con-
clude that the average prediction interval ater normalization
is significantly inerior to average interval beore normalization
(T=7.58, d=658, p-value
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The corresponding plots or the horizontal CoM angle xx
(top panel), Let Foot Back Let (LFBL) orce sensor (middle pa-
nel) and right leg phase right
(bottom panel) are depicted in
Figure 9 in orange. The robot reaches the start o the ramp at
t 30.6 s (C, blue dotted vertical marker), where the inclination
is still very sot. The inclination continues to get stronger and
more notorious, as can be seen by the large GRFLFBL
peak near
C. The robot eventually alls at t 31.2 s (indicated byD, solid
red vertical marker), with an increasing deviation in the valueo
xx.
On the other hand, ailure detection happens ahead o time.
When data normalization is not considered ailure prediction
occurs at t= 28.7 s (A, dotted-dashed grey vertical marker), and
at t= 29.04 s with data normalization (B, black dashed verti-
cal marker). This is not considered to be a relevant difference
- a window o 2.5 and 2.16 seconds beore alling, respectively,
is reasonable or corrective actions, although it will ultimately
always depend on the specific context. Even so, we consider the
benefits acquired with data normalization largely outweigh the
limitations encountered.
7.7. Size of Stored Data
With conventional ASMs, sensor data enclosing theP= 10 lo-
comotion periods corresponds to around 1600 data points
per sample or each sensor, and or both the means and
standard deviations have to be computed. This leads to
2 32 1600 = 102400 values to be stored (627 kB raw data).
Ater normalization, considering Kphase
= 200, each mean
and standard deviation are represented by 200 values (0.5%
intervals), which leads to 232200 = 12800, a decrease o
about 87.5% rom the initial data size.
Additionally, ater DMP regression, both and
are represented by the sets o D = 100 DMP parameters ound
through DMP regression. This leads to 2 32 100 = 6400 va-
lues, to which are added 3 DMP additional parameters o dam-
ping constantsx
, yand
yand the 64 starting values or each
DMP, which in all entails 6467 values (approx. 47 kB o raw data
- a 93.7% decrease to 6.3% o the original amount). It is notewor-
thy that this decrease ends up being dependent on the original
number o data periods recorded, and is even greater when
more than 10 periods are part o the original ASM. Besides, we
intend to make the ASM continuously or regularly updated by
the robot.
8. CONCLUSION AND FUTURE WORK
This work introduces systematic and automated guidelines
to build a skill memory rom sensor data, with periodic, time
consuming movements in mind, leading to a simplified and
phase-indexed representation. This allows to continuously
and periodically monitor the execution o a specific skill in
real time and predict nonconormity (such as robot alls),
allowing proper corrections to the robots behavior and mi-
nimization o unwanted consequences associated with mo-
vement ailure.
Conclusions are inerred rom simulations on the DARwIn-
OP robot. It is shown the potential in building a normalized ASM
and use it in ailure detection without significant losses in detec-
tion sensitivity and specificity - in act, i properly tuned, norma-
lized data can be as precise, albeit with a decreased advance in
detection. However, the authors consider the advantages brou-
ght by data normalization to largely outweigh the small decre-
ase in detection interval, as it adds flexibility and compactness
to the ASMs.
In the uture, it would be valuable to, instead o building
reerence performance confidence intervals, the procedure
would be inverted to arrive at reerence failure sensor oot-
prints, in order to properly identiy specific ailure conditions,that could be useul when identiying the cause o ailure.
These failure ASMs could be used either independently or in
conjunction with a library o skill ASMs. On a parallel line o
work, we have modified the CPG locomotion dynamics o our
locomotion system [14] to be easily modulated by a small set
o DMP systems, which are then parameterized by RL algori-
thms with the goal o adapting locomotion to different en-
vironments without changing the core dynamics. Ideally, we
expect to be able to use an ASM library to properly identiy the
current circumstances the robot is in and/or causes o ailure,
and appropriately load the correspondent DMP to cope withthe situation.
9. ACKNOWLEDGMENTS
This work is unded by FEDER Funding supported by the Ope-
rational Program Competitive Factors - COMPETE and National
Funding supported by the FCT - Portuguese Science Founda-
tion through project PTDC/EEACRO/100655/2008 and Project:
FCOMP-01-FEDER-0124-022674.
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JooMessias1,RodrigoVentura1,Pedro
Lima1,JooSequeira1,PauloAlvito2,CarlosMarques2,Pa
uloCarrio2
1InstituteforSystemsandRobotics,Instituto
SuperiorTcnico,UniversidadedeLisboa,{
jmessias,rodrigo.ventura,pal,jseq}@isr.ist.utl.pt
2IdMindEngenhariadeSistemas,Lda.,
{palvito,cmarques,pcarrico}@idmind.pt
Abstract Social Robotics is a rapidly expanding field o resear-
ch, but long-term results in real-world environments have been
limited. The MOnarCH project has the goal o studying the long-
term social dynamics o networked robot systems in human
environments. In this paper, we present the MOnarCH robotic
platorm to the research community. We discuss the constraints
involved in the design and operation o our social robots, and
describe in detail the platorm that has been built to accomo-
date the project goals while satisying those restrictions. We alsopresent some preliminary results o the navigation methodolo-
gies that are used to control the MOnarCH robotic platorms.
I. INTRODUCTION
Designing robots or social purposes has been a trendy topic
or the last decades. The literature in this area is huge and has
yielded valuable lessons [1], [2]. However, experiments where
robots and people coexisted or long periods o time, outside
lab environments, meaning periods longer than the transient
in the dynamics o human expectations, have seldom been
reported.MOnarCH1 (Multi-Robot Cognitive Systems Operating in
Hospitals, [3]) is an ongoing FP7 project with the goal o intro-
ducing (social) robots in real human social environments with
people and studying the establishment o relationships betwe-
en them.
The environment that acts as a case-study or the project is
the pediatric ward o an oncological hospital (IPOL). We intend
to introduce a team o robots in that environment, that coope-
ratively engage in activities aiming at improving the quality o
lie o inpatient children.
Key scientific hypotheses underlying the MOnarCH projectresearch are that (i) current technologies enable the acceptance
o robots by humans as peers, and (ii) interesting relationships
between robots and humans may emerge rom their interac-
tion. These hypotheses are supported by extensive existing
work on (i) autonomous and networked robotics, enabling
sophisticated perception and autonomous navigation, and (ii)
interaces or human-robot interaction and expressive robots.
MOnarCH addresses the link between these two areas, having
robots playing specific social roles, interacting with humans un-
der tight constraints and coping with the uncertainty common
in social environments.
The constraints o the social environment partially translate
into physical constraints on the robot platorms, such as its ma-
ximum allowable dimensions and velocities, and also behavio-
1 Reerence: FP7-ICT-2011-9-601033. Website: http://monarch-p7.eu/
ral constraints that can reflect on the methods that are used to
control those platorms, such as its navigation algorithms.
In this paper we present the MOnarCH robot platorm to the
research community. The platorm is well-suited to a wide range
o applications that extend beyond the MOnarCH case-study:
combining different high-level actuators and sensors, the base
can be used in the ofice, domestic or industrial environments
that are considered in the RoboCup@Home or @Work compe-
titions, or example.This document is organized as ollows. We will first provide
an overview o the constraints that were taken into account in
the design o this platorm (Section II). We will then describe the
robot hardware (Section III); and also o the methods that were
used to carry out its navigation (Section IV).
II. CONSTRAINTS ON ROBOT DESIGN & CONTROL
The MOnarCH project has a significant component o human-
robot interaction (HRI) to be carried out in a very specialized
social environment, namely that o IPOL pediatr ic ward. The na-
ture o this environment implies concerns and constraints onthe type o robots to be used, namely,
The range o allowable linear and angular velocities;
The volumetry o the ull robot;
Aesthetics;
Maximum height o the platorm;
Payload;
Power supply autonomy;
Sel-saety eatures;
Human-oriented saety eatures.
Moving naturally is an essential capability or a robot to be ableto survive in a social environment. In a sense, i a robot mo-
ves naturally, with velocities in the same order as those used
by humans moving, then other HRI interaces can be ocused
A robotic platformfor edutainment activitiesin a pediatric hospital
robtica100,3.oT
rimestred
e2015
Figure 1. A comparative study o linear velocities or common off-the-shel
platorms, and also or the MOnarCH robot platorm.
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and their behaviour does not need to depend on the motion
o the robot. Motion in 2D (as is the case in MOnarCH) is com-
pletely described by linear and angular velocities and hence the
ability to combine these two velocities determines the baseline
expressivity o the movement. This is a key aspect when desig-
ning a mobile platorm or socially embedded HRI purposes, as
in MOnarCH.
For example, in what concerns linear velocity, i a social ro-
bot playing with a child needs to ask him/her to wait becauseit cannot move as ast as the child then there is a significant
risk that the child looses interest in playing with the robot.
Moreover, uture interactions may be compromised because
the child may eel that the robot can not do a simple thing
such as moving the way he/she does. As or angular velocity,
its combination with the linear velocity determines i the robot
ollows the child graceully, i.e., with appropriate expressivity.
Motion capabilities are thus a basic eature that potentiates
the effect o all the other HRI interaces, namely voice, vision,
grasping, etc.
Differential drive mobile platorms can be ound off-theshelin a wide variety o ormats. Figure 1 shows the maximum va-
lues or the linear velocity o common robots. An adult moving
normally in an indoor environment reaches requently velocities
in the range o 2 to 2.5 m/s. Under teens children move slower
than adults when walking but may reach similar speeds when
running.
The physical presence o the robot has a large influence in
the way bystanders perceive the robot and its intentions. The
physical dimensions o the robot must not be perceived by chil-
dren neither as a menace nor as a physically diminished social
entity.
The average height o an under teen (11 year) is around1450mm and hence this determines the maximum height o
a MOnarCH robot. The volumetry is selected in order to be so-
cially acceptable and dynamically stable (not tilting under high
accelerations/decelerations). The ability to carry a large number
o sensors and interaces i a key eature in a social robot, this
meaning that payload is an important eature. Moreover, such
payload has to comply with the volumetry/height/aesthetics
concerns above.
Power supply autonomy severely constraints HRI capabili-
ties i the robot requires too much time to recharge batteries or
recharging occurs at an inadequate time. An HRI aware batterymanagement system limits the situations in which children may
perceive the robot as a flawed social entity.
O extreme importance are the saety eatures in the plat-
orm. In addition to basic physical saety o the people handling
the robots, saety concerns are directly related to Ethics issues
and o paramount importance when in social environments
such as that at IPOL.
Saety measures are embedded at both hardware and sot-
ware levels. Unexpected collisions trigger can be detected at
hardware level and bypass all decisions levels to stop the robot.
Each o the sotware layers has their own saety measures.
III. ROBOT DESCRIPTIONThe kinematics o a robotic platorm can greatly impact the type
o social interactions that it can be expected to perorm. As the
user case scenarios or the MOnarCH were being defined and
the constraints posed by the environment o operation were
being discussed, it became evident that the mobility capability
o the robots could be a critical issue to the achievement o pro-
ject goals. Based on this evidence, we have opted to develop an
omnidirectional robot platorm based on our Mecanum whe-
els, actuated by our independent motors. The use o this kind
o kinematics substantially increases the maneuverability and
perormance o the platorm. The development and assemblyo MOnarCH robots has been divided in two phases. The first
phase includes the platorm base mechanics with the motors,
batteries and low-level electronics. The resulting platorm can
be adapted to serve different applications. A second phase, whi-
ch specifically targets the MOnarCH scenario at IPOL, includes
the installation o high-level devices mounted over an upper
structure and the design o an outer shell. For this purpose, two
types o robots are being developed. Perception Oriented (PO)
robots will have as primary goal to act as active sensors. Social
interaction Oriented (SO) robots will target social interactions. As
aorementioned, the SO and PO robots are built over the same
platorm base, differing in the onboard equipment and externalappearance. An assembled platorm is shown in Figure 2. At this
time, the first phase o robot development has been concluded.
A. MOnarCH Robot Platform Base Main Features
All the robot platorms include the same basic configuration
which can be described through the ollowing design eatures:
Body: Polyacetal -POM (PolyOxyMethylene) 10 mm thick
plates; rigid PVC 4 and 6 mm; and transparent polycarbona-
te 2mm;
Robot kinematics: Omnidirectional 4 Mecanum wheels;
Robot weight: 24 Kg (with batteries); Payload capacity: 30 Kg;
Maximum Linear Speed: 2.5 m/s;
Maximum Angular Speed: 600 o/s;
Acceleration: 1 m/s2 (low-level programmed);
Emergency Stop Acceleration: -3.3 m/s2 (low-level pro-
grammed);
Mini-ITX computer Board with CPU, RAM and SSD;
Batteries:
Supports up to 4 batteries at the same time,
Capacity: (12v) 17-20 Ah 5.5 kg each,
Chemistry: lead acid or LiFePO4 block 12V batteries with
PCM,
Autonomy: 4 to 6 hours,
Actuators: 4 DC motors or locomotion;
Sensors:
Battery level,Figure 2. Assembled MOnarCH robot platorm.
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Motor encoders,
Omnidirectional bumper,
4 ground sensors,
12 sonars,
Laser Range Finder (5m range),
Temperature sensors to measure the motors and drivers
temperature,
Temperature and humidity sensor to measure the envi-
ronment conditions, Installed Electronics Boards:
Sensor & Management Board,
Motor Control Board,
Sonars Board,
Ground Sensor Board,
IMU Board.
B. MOnarCH Robot Upper Body
The upper body o the platorm will include different high-le-
vel devices. Some o these devices are still being defined and
need urther experiments to validate their use in the MOnarCHrobots.
Two depth cameras with microphone (Kinect type);
Three servo motors to actuate two robot arms and a head
(only SO robots);
One 10 touch-screen (only SO robots);
One pico-projector (only SO robots);
One RFID reader;
One Hagisonic StarGazer localization sensor;
Audio amplifier with speakers;
LEDs on the robot body;
Capacitive cells on the robot body;
C. Sensors
The robot is equipped with perception, navigation, interaction,
environment and low-level saety sensors. For locomotion the
robot uses encoders to control the velocity o the motors, and
or navigation it uses an inertial sensor to determine the angular
speed and a laser range finder to detect obstacles and the ge-
ometry o the environment. For perception and interaction, the
robot will use a depth camera or people tracking, ace analysis
and body gesture recognition, and also microphones. For envi-
ronmental sensing the robot will be equipped with temperatu-
re and humidity sensors. Finally, the bumpers and sonar sensorsprovide low-level saety sensing. To increase the robustness o
localization, some other sensors/solutions are also being evalu-
ated, e.g., RFID, IR and UWB.
We now list the sensors that are used onboard.
1) Navigation Sensors:The robot will navigate in the environ-
ment while making a usion o measures provided by different
sensors. The robot will be able to use a depth camera, a laser
range finder, encoders odometry and the IMU sensor to estima-
te its position and orientation. For obstacle avoidance, mapping
and localization it can use the laser and sonar sensors.
Inertial Sensor IMU: MPU6050;
Function: Orientation estimation;
Position: In the robots kinematic center;
Front 2D laser range-finder: Hokuyo URG-04LX-UG01;
Function: Mapping, localization and obstacle avoidance;
Position: Frontal and horizontal;
Sonar Sensors: Maxbotix EZ4;
Function: Obstacle detection (e.g.: glass wall or objects);
Position: Ring o 12 sonars around the robot;
Depth camera: Asus Xtion;
Function: Obstacle detection, space geometry analysis;
Position: Top o the robot pointing to the floor;
Sensors being evaluated: RFID, UWB, and ToF 3D cameras.
2) Perception and Interaction Sensors:The robot will make use o
a depth camera or people detection and sense visual user ee-dback or natural user interaction. It can also be used to detect
changes in the surrounding environment. The perception sen-
sors are the ollowing.
Depth camera: Asus Xtion;
Function: Interaction, people and gesture recognition;
Position: Top and looking ahead;
Microphone array: Asus Xtion;
Function: Sound eedback or natural user interaction;
Position: Turned to the users;
10 Touchscreen (or tablet);
Function: User eedback on specific contents;Position: Turned to the user;
Capacitive sensors;
Function: User eedback on specific points;
Position: Under the shell;
Other sensors still being evaluated: RFID and UWB.
3) Environment Sensors:The environment sensors are used to
detect environment variations that can affect the normal ope-
ration o the robot. These sensors are: temperature sensor and
humidity sensors.
4) Low-level Safety Sensors:The undamental sensors or low-
level saety are the sonar sensors, internal temperature sensors,
motor current sensing and the bumper ring switches.
D. Actuators
For actuation, the robot is equipped with locomotion and inte-
raction devices.
1) Locomotion Actuators: For locomotion, this omnidirec-
tional platorm uses our motors to drive its Mecanum wheels.
Four Maxon RE 35 90W 15V motor with a Maxon GP 32
HP 14:1 Gearbox and encoder HEDS 5540 with 500 pulses;
Function: Provide a omnidirectional locomotion sys-
tem to the robot;
Position: In the platorm, connected to the drive system.2) Interaction Actuators: Here ollows the list o interaction
devices. The robot is able to display the contents on the interac-
tion monitor or project them over a surace.
10 Monitor with Touchscreen (or tablet);
Function: Interaction with displayed contents (e.g., AR
contents);
Position: Front o the robot;
Video Projector (pico type);
Function: Projection o contents;
Position: Projecting to the ront o the robot;
Arms and head servo motors;
Function: Human robot interaction;
Position: Mounted on the robot body;
Body LED lights;
Function: Show robot expressions;
Position: Mounted on the robot body;
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Stereo Speakers;
Function: Content playback; robot communication;
Position: Turned to the user.
E. Electronic Power Architecture
The robot can be powered by several 12V 17-20AH batteries. It
uses one 12V battery to deliver power to the motor drivers. Up
to 3 other batteries to provide energy to all the other compu-
ters and electronic components. An individual charging unit isused inside the robot to charge each battery. The batteries and
the power in the robot is managed by the Sensor &Managment
Board that measures the battery levels, battery charge, and also
controls the units (motors, sensors, actuators and inverters)
powered by the batteries. All onboard electronic systems can
be powered by the battery system. The ATX computer power
supply provides regulated voltages (rom 5V to 12V). Figure 3
depicts the onboard power architecture. Several DC-DC conver-
ters are also be used to provide the necessary regulated power
or other DC-DC powered devices.
F. Low-level Communication Architecture
The onboard robot navigation computer communicates with
the two boards (Sensor& Management Board and the Motor
Controller Board) using 2 USB ports. In each board there are
USB-to-RS232 converters that convert the USB data packages
to serial RS232 packages or the board controllers. Each board
controller communicates with the other allowing the exchan-
ge o inormation between them. This communication chan-
nel allows the execution o low-level behaviours, or example,
react against an imminent collision, enter into charging mode
with motors shut down, reduce the motors velocity when the
batteries are low, or react to changes that can affect the robotsoperation, which is undamental to the improvement o the
overall system dependability. The main controller rom the
Sensor&Management Board communicates with other micro-
controllers using Inter-Integrated Circuit (I2C) communication
ports. The main controller acts as the master and the other
microcontrollers behave like slaves. The Sensor&Management
Board controls the battery management and charge, sensor
acquisition, devices actuators and sonar acquisition boards. The
Motor Controller Board connects to the PI Motor controllers and
also to temperature sensors. Each controller has a low-level ault
diagnosis that will check the operation state o each microcon-troller and also monitor all the communication between the
devices. The low-level communication architecture is depicted
in Figure 4.
G. High-level Communication Architecture
The MOnarCH robot connects to a local network. A wireless
Ethernet router provides the IP address to the onboard comput-
ers and allows the exchange o messages between them. The
Navigation Computer is connected to the navigation sensors
and to the platorm board controllers using USB ports. The In-
teraction Computer connects to the Projector using a HDMI
output and to the Sound System using the audio line out, and
will use USB connections to connect to the Interaction Board
that will control the body LEDs, the capacitive sensors and the
upper moving parts o the shell (arms and head). The high-level
communication architecture is depicted in Figure 5.
IV. NAVIGATION
For navigation we use a standard occupancy grid map [4], obtai-
ned rom off-the-shel SLAM sotware2This map is used both or
motion planning, using Fast Marching Method (FMM) [5], and
localization, using off-the-shel sotware3
.Motion planning is based on a FMM approach [5]. Unlike
other methods based on explicit path planning, e.g., RRT [6],
ollowed by path tracking, we adopt here a potential field ap-
proach. Given a map constraining the workspace o the robot,
together with a easible goal point, a (scalar) potential field u(x),
orx2R2, is constructed such that, given a current robot location
x(t), the path towards the goal results rom solving the ordinary
differential equation x(t)= u(x). In other words, given an arbi-
trary current location o the robotx, the robot should ollow a
gradient descent o the field u(x). Using potential fields or mo-
tion planning was proposed in the 80s [7] but they were ound
to be prone to local minima [8]. This problem can be solved by
the use o harmonic potential fields [9], however it does not gua-
2 GMapping (http://wiki.ros.org/gmapping, retrieved 16-Oct-2013).
3 AMCL, (http://wiki.ros.org/amcl, retrieved 16-Oct-2013).
Figure 3. MOnarCH robot power architecture.
Figure 4. Low-level communication architecture.
Figure 5. High-level communication architecture.
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a
g
en
f
4
obtca
rantee absence o local minima at the rontier. Thus, we decided
to employ a more recent approach [10]. The use o FMM provi-
des: (1) local minima ree path to goal across the gradient, (2)
allows the specification o a spatial cost unction, that introduces
a sot clearance to the environment obstacles, and (3) does not
require explicit path planning and trajectory tracking.
The FMM is based on the Level Set theory, that is, the re-
presentation o hypersuraces as the solution o an equationu(x)= C. The solution o the Eikonal equation
(1)
wherex2 is a domain, the initial hypersurace, andF (x)
is a cost unction, yields a field u(x) [5]. The level sets o this
field define hypersuraces u(x)=Co points that can be reached
with a minimal cost o C. The path that minimizes the integral
o the cost along the trajectory can be shown to correspond to
the solution o x(t)= u(x) with the initial condition ox(0)
set to the initial position and the initial condition u() = 0 set atthe goal4. Intuitively it corresponds to the propagation o a wave
ront, starting rom the initial hypersurace, and propagating
with speed 1/F(x). This path minimization is usually considered
a continuous space version o the Dijkstras algorithm. FMM is a
numerically eficient method to solve the Eikonal equation or
a domain discretized as a grid. Its computational complexity is
O(NlogN), whereNis the total amount o grid cells, which is
comparable to Dijkstras algorithm or sparse graphs.
Since FMM employs a grid discretization o space, it can be
directly applied to the occupancy grid map, where domain
corresponds to the ree space in the map. As cost unction we use
(2)
4 is set to the boundary o an arbitrarily small ball around the goal.
whereD(x) is the distance to the nearest occupied cell in the map
andDmax
is a threshold to clip the cost unction. This cost unction
induces a slower wave propagation near the obstacles, and thus
making the optimal path to display some clearance rom them.
The clipping atDmax
prevents the robot to navigate in the midd-
le o ree areas, regardless o their size. TheD(x) unction can be
directly obtained using an Euclidean Distance Transorm (EDT) al-
gorithm taking the occupied cells as boundary. Figure 6 illustratesthe results o this approach: the cost unction or the given map,
allowing a certain clearance rom mapped obstacles, is shown in
(a), rom which, given a goal location, a field u(x), shown in (b)
is obtained (the goal corresponds to the minimum value o the
field), and in (c) the real path taken by the robot is shown.
Using FMM on a previously constructed map does not ac-
count or unmapped or moving obstacles. Thus, the fieldv(x)
used to control the robot in real-time results rom combining
the field u(x) obtained rom FMM with a repulsive potential field
r(x) o obstacles sensed by the LRF. This repulsive field is obtained
rom running EDT on a small window around the robot, such thatthe value o r(x) corresponds to the minimum distance between
any obstacle and pointx. The fields are combined using
(3)
where is a parameters speciying the strength o the repulsive
field (higher values o tend to increase the clearance rom per-
ceived obstacles). Note that (3) destroys the property o a single
local minima o the field. We acknowledge the need to comple-
ment our navigation approach with a mechanism or detecting
and coping with stuck robot situations, such as replanning or
asking or help.
The method described above have proven to be very effec-
tive, even in cluttered environments ull o people crowded
around the robot. We have demoed this method on a public
event the European Researchers Night (September 27th,
(a) F(x) (b) u(x) (c) real path
Figure 6. Motion planning using FMM: (a) the cost unction F(x) (darker means a higher cost), (b) the solution field u(x) (level curves) together with the gradient descent
x(t)= u(x) solution (rom the right to the let), and (c) the real path traveled by the robot.
Figure 7.Trajectory o ISR-CoBot autonomously navigating along the IPOL premises. The task consisted in a sequence o waypoints.
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2013, in the Pavilion o Knowledge science museum, Lisbon) where
people rom all ages crowded around the robot.
We have also tested this method at IPOL, where we run exten sive
autonomous navigation tasks during several hours (Figure 7). These tests
were perormed on a previous platorm [11]. Even though that previous
platorm is differential, minor modifications on the guidance method
were required to adapt it to the MOnarCH platorm. A video showcasing
the application o these methods to the autonomous navigation o the
MOnarCH platorm can be ound at: http://tinyurl.com/olounbn.
V. CONCLUSIONS AND FUTURE WORK
In this work, we have introduced the robotic platorm that was develo-
ped in the context o the MOnarCH project. This development explicitly
took into account a set o constraints that are induced by the social na-
ture o the projects case-study environment, namely, the pediatric ward
at IPOL. We described these constraints; detailed the hardware that was
(and is being) included in the robotic platorm; and presented prelimi-
nary results regarding the methods that were developed or reliable ro-
bot navigation.The qualities o the MOnarCH platorm make it a good choice or
other applications beyond the projects case-study, such as the Robo-
Cup@Home or @Work scenarios.
As immediate uture work, we will integrate the high-level sensors
and devices discussed in Section III, as part o the second phase o robot
development. This will endow the robot platorm with HRI capabilities,
establishing a basis or the uture development o the socially-aware in-
teraction methods that are crucial to the outcome o the project.
ACKNOWLEDGMENTS
Work supported by FCT projects PEst-OE/EEI/LA0009/2013 and FP7-ICT-9-2011-601033 (MOnarCH).
REFERENCES
[1] C. Breazeal, Designing sociable robots. The MIT Press, 2002;
[2] G. Metta, G. Sandini, D. Vernon, L. Natale, and F. Nori, The iCub humanoid robot: an open
platorm or research in embodied cognition, in Procs. of the 8th Workshop on Performan-
ce Metrics for Intelligent Systems, PerMIS 08, 2008, pp. 5056;
[3] J. Sequeira, P. Lima, A. Safiotti, V. Gonzalez-Pacheco, and M. A. Salichs, MOnarCH: Multi-
robot cognitive systems operating in hospitals, in ICRA 2013 Workshop on Many Robot
Systems, 2013;
[4] A. Eles, Using occupancy grids or mobile robot perception and navigation, IEEE Com-
puter, vol. 22, no. 6, pp. 4657, 1989;
[5] J. A. Sethian, Fast marching methods,SIAM review, vol. 41, no. 2, pp. 199235, 1999;
[6] S. LaValle and J. Kuffner Jr, Randomized kinodynamic planning,The International Journal
of Robotics Research, vol. 20, no. 5, pp. 378400, 2001;
[7] J. Borenstein and Y. Koren, Real-time obstacle avoidance or ast mobile robots, IEEE
Transactions on Systems, Man and Cybernetics , vol. 19, no. 5, pp. 11791187, 1989;
[8] Y. Koren and J. Borenstein, Potential field methods and their inherent limitations ormobile robot navigation, in Proceedings of the IEEE International Conference on Robotics
and Automation (ICRA-91), 1991, pp. 13981404;
[9] J. Kim and P. Khosla, Real-time obstacle avoidance using harmonic potential unctions,
IEEE Transactions on Robotics and Automation, vol. 8, no. 3, pp. 338349, 1992;
[10] S. Garrido, L. Moreno, D. Blanco, and M. L. Munoz, Sensor-based global planning or
mobile robot navigation, Robotica, vol. 25, no. 2, pp. 189199, 2007M
[11] R. Ventura, New Trends on Medical and Service Robots: Challenges and Solutions, ser. MMS.
Springer, 2014, ch. Two Faces o Human-robot Interaction: Field and Service robots.
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obtca
empeende
e
n
a
Frederico Lucas o empreendedor responsvel pelo projeto Novos
Povoadores, que presta apoio a empresrios que pretendammigrar com as suas amlias para territrio rural. Nascido
em Lisboa, em 1972, o autor dos conceitos territoriais desoftwareterritorial e economia DNS. Territorial Developer
em projetos de dinamizao territorial em www.inoex.pt e considerado como um visionrio de um uturo com grandes
mudanas e criao de novos paradigmas.
Sentido da vida
A robotizao das bricas tem sido ob-
servada com grande reserva por uma
parte significativa da populao mundial.
O filme Os tempos modernos de CharlieChaplin no compreendido por todos.
A histria do mundo no se az com
recordistas de linhas de montagem. So
tareas banais e repetitivas, que algum
equipamento ter a capacidade de
executar.
Complexas so as atividades que exi-
gem cultura, sensibilidade e conscincia.
Valores que uma mquina no saber in-
terpretar, e que dierenciam cada um de
ns, consequncia dos nossos percursose contextos.
A robotizao um aliado do Ser Hu-
mano, porque o liberta de tareas manu-
ais. Conduz a Humanidade para as tareas
inteletuais, a sua vocao.
Na Europa, este processo decorre h
vrias dcadas, e os elevados nveis de
desemprego espalham algum receio na
sociedade. Estamos a alhar no essencial:
legtimo e natural, que a gerao dos
nossos filhos venha a trabalhar quatro
horas por dia. E que essas horas sejamtempo de criao, em oposio a tareas
burocrticas ou repetitivas, mas sempre
mal pagas.
O poder de compra descer em con-
traciclo com o poder da mente.
As fbricas retiraram milhes
de chineses dos campos,os robots esto a entrarnas fbricas e os operrios
no querem voltar a seragricultores.
Joo Vale de Almeida,
Diplomata
Complexas so as atividadesque exigem cultura, sensibilidade
e conscincia. Valores que uma
mquina no saber interpretar,e que diferenciam cada um de ns,consequncia dos nossos percursos
e contextos. A robotizao umaliado do Ser Humano, porque
o liberta de tarefas manuais.Conduz a Humanidade para
as tarefas inteletuais, sua vocao."
As bricas que outrora poluram rios,
estaro no uturo a azer o processo in-verso: a recolher resduos nesses rios e a
produzir capas para iPhones com esses
detritos, num modelo conhecido por
upcycling.
Esses rios regressaro sua uno:
espao de lazer para o reino animal, in-
cluindo Seres Humanos, que deixaram
de os requentar por falta de tempo.
Pelo exposto, sou um deensor da
robotizao das indstrias. Estejam os
robotsao servio da pigmentao de car-
roarias ou a filtrar mensagens de spam
nas nossas caixas de correio.
Acredito que a Europa, antes da
China, saber trilhar este caminho, re-
centrando a Humanidade no Sentido da
Vida.
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A
MA
E
N
AdrianoA.Santos
DepartamentodeEngenhariaMecnica
InstitutoPolitcnicodoPorto
Automatizao de sistemasde bombagem
INTRODUOA automatizao de uma rede de rega
ou distribuio de gua pode, em geral,
ser realizada em escalas distintas. Se nos
sistemas de rega a mxima automatiza-
o se consegue atravs da programa-
o das regas, da ertilizao e outros
aspetos relacionados com a recolha de
dados agrometeorolgicos e de cultivo,
nos sistemas de distribuio a mxima
automatizao consegue-se atravs do
controlo e monitorizao dos ativos re-motos como as estaes de bombagem,
de elevao e das estaes de tratamen-
to. A um nvel mais baixo, o controlo po-
der ser realizado localmente atravs de
um programador que, ao nvel da rega,
controla cada um dos hidratantes e o
conjunto de vlvulas e eletrovlvulas. Ou
seja, pode-se automatizar uma rede de
distribuio de gua, quer seja de rega
quer seja domstica, recorrendo a um
computador central que, com base, por
exemplo, num sistema SCADA (Supervi-sory Control and Data Acquisition), contro-
la os hidratantes, as vlvulas e os sistemas
remotos.
Por ltimo, e no menos importante,
as estaes de bombagem que tambm
podem e devem ser reguladas e contro-
tizao completa da estao de bomba-gem, mediante a variao de velocidade
das bombas, sensores de presso e cau-
dalmetros controlados por um autma-
to programvel, PLC (Programmable Logic
Controller).
AUTMATO PROGRAMVEL
Um controlador lgico programvel, nor-
malmente conhecido como PLC, um
equipamento idealizado para o controlode processo tendo como base instru-
es lgicas programadas atravs de um
software especfico. So equipamentos
de reduzidas dimenses que, instalados
no quadro de comando, permitem con-
trolar o processo e a comunicao com
o exterior e receber ordens atravs dos
canais de comunicao o que permite
a configurao de sistemas de teleco-
mando e de telemedida, dando lugar
ao comando centralizado. Estes, embora
possam possuir capacidades muito supe-riores s exigidas para este tipo de con-
trolo, apresentam uma elevada capaci-
dade de processamento, uncionamento
em modo local, inteligncia distribuda e
comunicao via rdio ou por cablagem
(concentrador ou hub).
Estes elementos, praticamente indis-
pensveis nos processos de automatiza-
o, so caraterizados por uma estrutura
modular que permite o crescimento de
acordo com as necessidades do proces-so, so robustos, possuem elevada capa-
cidade para a implementao de progra-
mas complexos, acilidade de correo
e modificao dos programas e baixo
custo (Figura 1). Os PLCs podem ser, per-
ladas, adaptando-se s variaes de cau-dal e de presso da rede. Estas so cons-
titudas, normalmente, por uma ou mais
bombas encarregadas de transormar a
energia mecnica recebida dos sistemas
de acionamento em energia hidrulica.
A energia hidrulica ser, ento, utiliza-
da para aspirar gua subterrnea de um
poo, elevar gua de uma cota inerior
para uma cota superior ou injetar uma
presso adicional na instalao. Assim,
e pelo que oi anteriormente exposto,pode-se dizer que a automatizao mais
bsica de um sistema de bombagem
consiste no arranque e paragem auto-
mtica segundo uma regulao horria.
A automatizao mais completa consis-
tir no controlo automtico do arranque
e paragem da bomba, monitorizao da
abertura e echo das vlvulas, tempos de
abertura e echo das mesmas, nvel de
gua, entre outros.
Por outro lado h que considerar
que a aspirao e a elevao de cotas,do ponto de vista uncional, trabalha
com parmetros fixos, altura e caudal, e
com pequenas oscilaes devido va-
riao do nvel de gua e, como tal, no
necessitam de um sistema de regulao
de caudal, trabalhando sempre perto da
seu rendimento mximo. Ao contrrio do
que se passa nos sistemas descritos ante-
riormente, as redes de distribuio sobre
presso esto sujeitas a variaes de cau-
dal ao longo da sua utilizao, pelo queestas necessitaro dum sistema de regu-
lao de caudal que adeque a presso e
o caudal, impulsionado pelas bombas,
procura instantnea da rede. Neste caso,
uma boa regulao requer uma automa-
Figura 1. Autmato programvel (PLC), mdulo de comunicao, CPU e mdulo digital de I/O (Siemens).
"A automatizao de uma
rede de rega ou distribuio degua pode, em geral, ser realizadaem escalas distintas.
Se nos sistemas de regaa mxima automatizao
se consegue atravsda programao das regas,
da fertilizao e outros aspetosrelacionados com a recolha
de dados agrometeorolgicose de cultivo, nos sistemas de
distribuio a mxima automatizaconsegue-se atravs do controlo
e monitorizao dos ativosremotos como sejam as estaes
de bombagem, de elevao e dasestaes de tratamento."
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AUTOMAOECONTROLO
19
robtica
eitamente, utilizados nas estaes de
bombagem no controlo do arranque e
paragem das bombas, na abertura e e-
cho das eletrovlvulas, no nvel de aspi-
rao e nvel dos depsitos de armazena-
mento, na regulao do caudal, presso,
entre outros.No que se reere ao uncionamento
dos autmatos programveis este rela-
tivamente simples. As entradas e sadas
do sistema so fisicamente ligadas aos
dispositivos de campo das mquinas,
aquisio de sinais, e aos processos a
controlar, envio de sinais.
As entradas transerem para a uni-
dade central do autmato inormaes
sobre o uncionamento do processo que
podem ser sinais discretos ou analgicos
(pressstatos, sondas de nvel, transduto-res de presso, e outros). O CPU, por sua
vez, em uno do programa lgico car-
regado, envia para as sadas inormaes,
tambm elas discretas ou analgicas,
para eetuar o controlo dos dispositivos
(rels das bombas, solenides, aciona-
mento de vlvulas, variao de veloci-
dade das bombas, entre outros). Assim,
e durante o tempo de uncionamento
deste, o ciclo de trabalho do autmato
executar trs aes cclicas, preponde-rantes para o controlo do processo como
a leitura das variveis de entrada, a exe-
cuo do programa lgico de controlo e
a escrita das variveis de sada, como se
depreende do esquema representado na
Figura 2.
Esta leitura sequencial das entradas,
execuo do programa e atualizao das
sadas conhecida como ciclo de varri-
mento (Scan) onde as entradas digitais
do tipo On/Off, permitem ler o estado
dos pressstatos, contactores, botes
de presso, comutadores, entre outros,
ou seja, o estado dos equipamentos li-
gados (On) ou desligados (Off). Por sua
vez, as entradas analgicas permitem
conhecer, em modo contnuo, quer em
mA (miliAmperes) quer em V (Voltes), o
valor dos sinais emitidos pelo controlo
de presso, caudal, volume, temperatura,
entre outros, ou seja, receber sinais do
tipo analgico.As sadas digitais so utilizadas em
manobras de abertura e echo de eletro-
vlvulas, arranque e paragem de bom-
bas, vlvulas motorizadas bem como de
sistemas de sinalizao e de piloto. As
sadas analgicas so utlizadas na atua-
o de variadores de requncia, variao
da velocidade das bombas, e posio de
abertura de vlvulas motorizadas, servo-
-vlvulas, entre outros.
VARIADORES DE FREQUNCIA
Os variadores de requncia so utiliza-
dos para variar a velocidade de uncio-
namento dos motores assncronos de
Corrente Alternada (CA) utilizados no
acionamento das bombas hidrulicas.
O princpio de uncionamento des-
tes equipamentos baseia-se no controlo
da velocidade e binrio do motor, por
intermdio de um sistema de comando
eletrnico. Quando um motor de indu-
o colocado em marcha, o seu ponto
de operao ajusta-se ao fim de algumtempo de uncionamento. Este ajuste
depende da voltagem (V) e da requn-
cia () aplicada ao motor. O ajuste pode
ser obtido atravs da curva caraterstica
do motor, ou seja, da relao V/ (V/Hz)
(Figura 3).
Note-se que a relao V/ linear
entre =0 e a requncia de base (50 Hz).
A tenso aplicada ao motor aumenta
linearmente at aos 50 Hz, situao em
que o motor atinge a sua tenso nominal(tenso da rede). At aos 50 Hz o bin-
rio desenvolvido pelo motor constante
com uma potncia crescente. Acima de
50 Hz, a tenso mantm-se constante e
igual ao valor nominal com binrio de-
crescente (zona a tracejado). O aumento
da requncia implica o aumento da ve-
locidade, o que se traduz na diminuio
do binrio.
O retificador gera uma Corrente Con-
tnua (DC) que posteriormente filtrada
e introduzida no inversor. A unidade decontrolo , normalmente, constituda por
um modulador PWM, limitador de cor-
rente e um controlador de velocidade do
tipo (PI) (Figura 4). O modo de operao
pode ser manual ou automtico segun-
do as necessidades do processo pelo
que estes podem ser operados por com-
putadores, PLC, atravs de sinais digitais e
analgicos ou de orma manual.
A utilizao de variadores de re-
quncia nos mais diversos processos ,hoje em dia, uma prtica corrente devi-
do, essencialmente, ao seu baixo custo,
melhoria energtica resultante da sua
utilizao e acilidade de se poderem
instalar com qualquer motor assncrono
Figura 2. Esquema geral de uncionamento de um autmato.
PROCESSO(Dispositivos de atuao)
ENTRADA
(Sensores, sinaisdigitais e analgicos)
AUTMATO
(Lgica programada)
SADA
(Atuadores, sinaisdigitais e analgicos)
Figura 4.Diagrama de blocos de um variador de requncia do tipo V/.
Barramento DC
FiltroRetificador
Redede
alimentao
Tensode
frequnciafixas
Alimen
taomotor
Tensoefrequnciavarivel
Inversor
Unidade de controlo microprocessada
0
V
M
10 20 30 40 50 60 70 Hz
Figura 3.Curva V/ e binrio para um motor
de induo.
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MA
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no interior dos quadros de controlo dos
processos (Figura 5).
No entanto, a variao de velocidade
de rotao do motor produz uma modi-
ficao da curva caraterstica da bomba
dando lugar a curvas paralelas curva davelocidade nominal, como se pode ver
na Figura 6, diminuindo o rendimento
da bomba que , claramente, mais baixo
do que o rendimento terico. A perda
de rendimento deriva principalmente
do rendimento do prprio variador de
requncia e, esta perda ser tanto maior
quanto mais baixa or a velocidade de ro-
tao da bomba.
AUTOMATIZAO DE SISTEMAS
DE BOMBAGEM
Como j oi reerido a automatizao dos
sistemas de bombagem pode ser realiza-
da com dierentes graus de automatiza-
o. Em geral, o grau de automatizaoser tanto maior, quanto maior or a pro-
undidade do poo e o caudal da bomba,
a cota de elevao e a presso de injeo
na rede de distribuio.
Por outro lado, e em qualquer um
destes processos, o arranque e a para-
gem, totalmente automatizada, podero
ser controlados por um autmato tendo
em considerao que a abertura e o e-
cho das vlvulas devem ser temporiza-
dos de modo a proteger o binmio mo-
tor-bomba, a tubagem e evitar golpes deariete (martelo ou choque hidrulico) na
ase de paragem dos processos.
No obstante, este tipo de automati-
zao com abertura e echo temporiza-
do de vlvulas, tem sido substitudo por
sistemas de variao de requncia. Os
variadores permitem o arranque e a para-
gem suave dos motores protegendo-os
de sobreintensidades e dos golpes de
ariete. Esta proteo conseguida com
o arranque e a paragem progressiva do
motor da bomba acionado pelo variador
segundo o programa de arranque e de
paragem estabelecido.
Outros pontos devem ser considera-
dos no processo de automatizao dos
sistemas de bombagem como, e no
menos importantes, os reservatrios,as vlvulas, os sensores (nvel, presso,
caudal, e outros) bem como as aes
de segurana e emergncia. Assim, e
no que se reere s aes de segurana
e de emergncia programadas no PLC,
estas passaro pela paragem de emer-
gncia por alta de comprovao da
abertura e echo das vlvulas, sinal de
retorno da posio, por valor de presso
do pressstato superior ao mximo ou
mnimo predefinido incluindo os nveisde gua nos poos e nos reservatrios,
isto , alha dos sinais das sondas de n-
vel mnimo e mximo de gua respon-
sveis pela paragem da bomba, perante
o nvel mnimo de gua e, no caso de
enchimento, evitar que a gua transbor-
de no reservatrio.
No que se reere s unes de con-
trolo e regulao estas devem incidir
no s sobre a regulao do caudal que
deve ser realizada em situaes em que
o ornecimento da gua inerior s ne-cessidades dos consumidores, nas horas
de ponta, aumentando o ornecimento e
diminuindo-o nas horas de menor con-
sumo, preservao dos nveis de gua,
mas tambm sobre o ator energtico e
na sua respetiva regulao que passar
pelo uncionamento dos sistemas, sem-
pre que possvel, durante as horas de
energia mais barata, ou seja, nos pero-
Figura 7.Esquema geral de um sistema de bombagem de injeo direta na rede.
Figura 5.Variadores de requncia (Siemens).
Figura 6.Curvas caratersticas de uma bomba
a dierentes velocidades de acionamento.
H (m)
P (kW)
Q (l/min)
1100 rpm
1150 rpm
1400 rpm
1400rpm
1450 rpm
1450rpm
0'70'75 0'8
0'80'75
0'81
500 1000
100
100200300400
75
50
1100rpm
Sonda de nvel mnimoSonda de
nvel de guaSonda de nvel