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Development of a Firefighting Robot forEducational Competitions
Taiser T. T. BarrosCarlos Tannhauser Professional Education School
SENAISanta Cruz do Sul, Brazil
Walter Fetter LagesElectrical Engineering Department
Federal University of Rio Grande do SulPorto Alegre, Brazil
Abstract—This work presents the design and assembling detailsof a robot developed to take part in an educational roboticcompetition. A control law based on Lyapunov theory wasdeveloped and implemented on a Programmable Logic Controllerto control the robot.
I. INTRODUCTION
Every two years the Brazilian institutionServiço Nacionalde Aprendizagem Industrial(SENAI) — National Serviceof Industrial Apprenticeship — promotes the KnowledgeOlympiad, a Professional Education Competition, where stu-dents can show their skills in specific areas (like mechatron-ics). The competition lasts for four days and in its course thestudents need to solve problems usually seen in real industrialenvironments. The performance of students is rated accordingto technical items that come from industry needs [1].Industrial robotic is one area of the Knowledge Olympiad
and includes theoretical and practical knowledge in manyfields like mechanical, electric, electronic and pneumaticsys-tems. It follows the format of other Robotic competitions likeFIRST — For Inspiration and Recognition of Science andTechnology — that aims to inspire young people to interestand participate in science and technology [2] and NRC —National Robotics Challenge — that is promoting educationalrobotics since 1986 in a competition that actually offers twelverobotics contests [3].In the Industrial robotic area, competition teams are com-
posed of three students that need to develop a mobile robotable to move in a field with or without obstacles, solving aproposed problem. The Knowledge Olympiad is a competitionfor students of SENAI mid-level technician courses, wherethey can share their knowledge and learn with other students.The teams are always renewed every two years and theircomponents need to be less twenty one years old.The competition has two stages: regional (on each state)
and national. The champions from regional steps go to thenational one, and champions from national competition canenjoy an international competition: The WorldSkills, thatis acompetition with more than 60 years and occurs every twoyears joining students — from 52 countries — that competein skills of various areas testing themselves against demandinginternational standards [4].
On 2012 regional competition, the proposed problem de-manded the robot to be able to identify and extinguish firefocuses. The competition was divided in four different mod-ules, where the robot needed to search for candles (used asfire focus).In the first module, the team needs to assembly the robot.
Thereafter, the assembled robot is weighted and the team needsto show that the robot is able to move autonomously for onemeter on the competition field. The second module is designedto test the capacity of the robot to move on the competitionfield and find the points of possible fire focuses using a soundsignal to indicates when they are found.In the third module, the robot needs to find the fire focuses
again, but at this time they could be activated and the robotneeds to signal this condition. And finally, in the fourthmodule, the tasks are equal to those in third module, butat this time the robot needs to climb up and down in twoslopes located randomly on the competition field. At the endof the competition modules, all the points from each team arecalculated and the winner is known.Figure 1 shows the competition field that presents different
lanes for the robot to move from an initial point (P0) to the firefocuses points (F1 and F2). The field lanes differs in lengthsand angles and each team can choose the best path for theirrobot, considering items like path difficulty and the shortesttime. Figure 2 shows the actual robot performing a competitionstep.The remainder of this paper is as follows: Section II treats
the robot structure, presenting the mechanical structure andthe control architecture. Section III proposes a control law tobe implemented in the Programmable Logic Controller (PLC),and section IV presents some simulation results.
II. DESIGN OF THEROBOT
Many technical concepts from different areas like mechan-ical, electric, electronic and pneumatic systems were usedto project an build the robot. This allowed students teamto exercise their technical knowledge, as idealized by theKnowledge Olympiad. This section briefly describes the robot.Figure 3 shows the robot in a exploded view and figure 4
shows its 3D view. All robot parts were designed using the
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Fig. 1. Competition field.
Fig. 2. Robot.
Solidworks CAD software. Basically, the robot structure canbe described by referring the numbers seem on figure 3:
• Pneumatic components (1)• Transmission components (2)• Batteries (3)• Flame detector sensor board (4)• Step motors (5)• Robot chassis (6)• Robot controller (7)• Drives (8)
A. Robot Mechanical Structure
1) Pneumatic components:Based on the tested methods,the best way to extinguish fire was by means of compressedair. This way, a pneumatic system was developed using apneumatic reservoir (with pressure calibrated at 8 bar) andone pneumatic solenoid valve used to shoot air.Every time the robot find a fire focus one air shot is released.
The reservoir is sized to release at least twenty shots.2) Transmission components:The transmission between
step motors and robot wheels uses a couple of gears with1:2 relation driven by a synchronized belt.
3) Batteries: Two sets of batteries (24V = 12V + 12V)are used to power the robot. One is connected to drives andmotors, while the other one provides PLC power, thus avoidingnoise problem due to current peak while starting the motors.
4) Flame detector sensor board:Two methods were testedto allow robot to detect fire focuses:
Fig. 3. Robot exploded 3D view.
Fig. 4. Robot 3D view.
• Detecting temperature• Infra-red DetectionThe infra-red proved to be better, as its response was
faster and more accurate than temperature detection. Infra-redsensor board development used basically some pairs of LED-photodetectors. Figure 5 shows the circuit board developedtointegrate the sensors.
5) Step motors:The total mass allowed for the robots incompetition is 25 Kg. Step motors were sized by compromis-ing their weight against the torque needed to move the robot.In fact, the motors were oversized to avoid problems whenmoving the robot.
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Fig. 5. Flame detector sensor board.
6) Robot chassis:The robot chassis was designed andmilled by the students. Only components like belts, gearsand bearings were purchased and used off-the-shelf. Figure6shows a piece designed to the robot chassis.
Fig. 6. A robot chassis piece.
B. Robot off-the-shelf Controller
1) PLC: To use industrial components, a PLC was chosen.A PLC is an equipment with many capabilities like networkcommunication and complex calculation. It is possible toforesee that they will engage important place in the factoryofthe future [5]. The use of a PLC provided dependable hardwareleaving the students free do develop the control software.The PLC is a SIEMENS S7-1200 family CPU [6], specif-
ically the CPU 1214C, that among other things, offers 2PTO (Pulse Train Outputs) used to generate pulses to controlthe drives. The software was developed using the LADDERlanguage. The PLC development environment is shown onfigure 7.
2) Drives: The drives used in the robot actuate the stepmotors by using the micro step technique to increase steppingaccuracy [7]. The drive model used allows up to 25600 stepsper motor revolution.
III. ROBOT CONTROL SYSTEM
A. Robot Open Loop Guidance
The main idea used to guide the robot was based on thecompetition field design. On figure 1, three main points are
shown: P0 - the start/end point, and F1-F2 (where the candlesare placed).For each competition module, the robot needed to start from
P0, and verify if a fire focus was present at F1 or at F2 (itwas possible to exist only at F1 or at F2, or moreover at F1and at F2), returning to P0 after that. Then, the robot softwarewas designed by following coordinate points.One crucial point to get good results was the initial place-
ment on the competition field, as the robot is guided by relativecoordinates, the initial point always need to be the same,otherwise the robot would loose his position and do not returnfor P0.This open loop guidance, served most the time during the
competition, but one situation has proved that this controlhasparticular week points like when the robot needed to rise upand go down from a ramp. While going down, the robot had alittle sliding and switched its course. This way, the end pointP0 was not reached.The open loop guidance follows the idea from figure 8. The
PLC calculates each point from the coordinate system, sendsaPTO signal to the drives, which generate the number of pulsesneeded to drive the step motors. Besides, the PLC monitorsthe Flame Sensor Board to detect fire focuses, and if necessaryactuates the pneumatic solenoid valve to extinguish flames.
Fig. 8. Robot open loop guidance.
B. Robot Closed Loop Control
In order to improve the robot performance, a closed loopcontrol system was developed (figure 9). To allow this newsystem in the robot, a pair of encoders will be used to sensethe displacement of each wheel.
Fig. 9. Robot closed loop control.
1) Robot Mathematical Model:This work describes a dif-ferential drive wheeled mobile robot as that one depicted infigure 10. Its kinematic model [8] describes the robot positionand orientation given linear and angular velocities:
x = f(x, u) =
cosx3 0sinx3 00 1
u (1)
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Fig. 7. PLC program development environment.
wherex =[
x1 x2 x3]T
is system state vector andu =[
u1 u2]T
is the input vector. The state variablesx1 andx2are the plane coordinates,x3 is the orientation angle, and theinput variablesu1 andu2 are the linear and angular speeds.Figure 10 shows the system coordinates, whereXc1 andXc2
are the axes of the robot andX1 andX2 form the inertialcoordinate system. Time dependency is omitted.
O X1
X2
Xc1, u1Xc2
x1
x2
X
x3, u2
Fig. 10. Differential-drive mobile robot coordinates.
Differential-drive mobile robots are nonholonomic sys-tems [9]. An important general statement on the control ofnonholonomic systems has been made by Brockett [10], whohas shown that it is not possible to asymptotically stabilize
the system at an arbitrary point through a time-invariant,smooth state feedback law. In spite of it, the system iscontrollable [11].Ways around Brockett’s conditions for asymptotic stability
are time-variant control [12], [13], [14], [15], non-smoothcontrol [11], [16], [17] and hybrid control laws [18]. In thispaper, we will obtain a set of possible input signals basedon non-smooth control law which is obtained by a non-smooth coordinate transformation. A general way of designingcontrol laws for nonholonomic systems through non-smoothcoordinate transformations was presented by [11]. We haveconsidered a mapping from the state space to the input spaceas presented by [8].The mappings from the system state to the input space
which are used for point stabilization are such that the statespace origin is made asymptotically stable. If we representthe mapping asg : X → U, x ∈ X andu ∈ U, then theautonomous system
x = f (x, g(x)) (2)
wheref(·, ·) is described by (1), is asymptotically stable atthe origin. However, it is of interest to stabilize the robotatany pointxr, which means any given position and orientation[
xr1 xr2 xr3]T.This can be accomplished by the coordi-
nate changex(x,xr), obtained by setting a new referenceframe Xr1Xr2 at the reference position
[
xr1 xr2]T
withan anglexr3 , according to figure 11. Thus, the coordinatechange fromX1X2 to Xr1Xr2 consists of a translation and arotation of anglexr3 . It is readily verified thatx3 = x3−xr3 .
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Therefore, the coordinate changex(·, ·) is obtained by thetransformation
x =
[
R(xr3) 0
0 1
]
(x− xr) (3)
whereR(xr3) is a 2-D rotation matrix, that is,
R(xr3) =
[
cosxr3 sinxr3− sinxr3 cosxr3
]
. (4)
Hence, if the systemx = f (x, g(x)) is stable atx = 0,then x = f (x, g(x)) is stable atx = 0. Therefore, in orderto stabilize the system at any arbitrary pointxr based on acontrol law g that leads the state to the origin, it suffices touseg(x).
O X1
X2
Xc1, u1
Xc2
x1
x2
X
x3, u2
x1
x2
x2
x3
xr1
xr2
xr3
xr3
Xr1
Xr2
Xr
Fig. 11. Robot coordinates with respect to the reference frame.
By considering a coordinate change [8],
e =√
x21+ x2
2(5)
ψ = atan2(x2, x1) (6)
α = x3 − ψ. (7)
the system model (1) can be rewritten as
e = u1 cosα
ψ = u1sinα
e
α = −u1sinα
e+ u2.
(8)
Then, given a Lyapunov candidate function
V =1
2λe2 +
1
2(α2 + hψ2), (9)
it can be shown that the input signalu(k)
u1 = −γ1e cosα (10)
u2 = −γ2α− γ1 cosαsinα
α(α− hψ), (11)
with h, γ1, γ2 > 0, makes (8) asymptotically stable [8]. Wenote that even though the model (8) is discontinuous at theorigin, due toe in the denominator, the closed loop system isnot. The term in the denominator is canceled in closed loopbecause (10) containse as a factor.Sinceu1 andu2 were calculated according to (10) and (11)
the angular speed of right wheel (ωr ) and left wheel (ωl) canbe obtained by:
ωr =u1 + u2
b2
rr(12)
ωl =u1 − u2
b2
rl(13)
whereb is the distance between wheels andrr , rl are the rightand left wheel radii. Once the angular speed of each wheel isknown, the number of control pulses that need to be sent tothe drives to actuate the right (nr) and left (nl) wheels can becomputed by:
nr =ωr
2πNrT (14)
nl =ωl
2πNlT (15)
where Nr, Nl are the number of pulses to generate onecomplete motor revolution andT is the sampling period.The robot position can be estimated based on following
odometry expressions:
xc[k + 1] = xc[k] + ∆D[k] cos
(
θc[k] +∆θ[k]
2
)
(16)
yc[k + 1] = yc[k] + ∆D[k] sin
(
θc[k] +∆θ[k]
2
)
(17)
θc[k + 1] = θc[k] + ∆θ[k] (18)
where∆D is the robot linear displacement and∆θ is the robotangular displacement given by:
∆D[k] =
(
npr
Npr2πrr +
npl
Npl2πrl
)
2(19)
∆θ[k] =
(
npr
Npr2πrr −
npl
Npl2πrl
)
b(20)
wherenpr, npl are the number of pulses read from encoderscoupled to each wheel, andNpr, Npl the number of encoderpulses per revolution.
IV. SIMULATION RESULTS
Two simulations were made using MATLAB to verify robotmathematical model behavior:
• Point Stabilization• Path tracking
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A. Point Stabilization
On this simulation it is possible to verify the robot behaviorwhen moving from point[0, 0, 0]T to the reference point[2, 2, π]T . Figure 12 shows the robot displacement on x axis,figure 13 on y axis, figure 14 the robot angle behavior whilethis displacement and figure 15 the robot displacement inCartesian space, where it is possible to observe that thereference point was reached. The continuous line representsthe system output simulated and points represent the estimatedrobot position.Robot linear and angular speeds are depicted on figure 16
and figure 17 shows the simulated encoder reading pulses ofright and left wheels.
0 2 4 6 8 10 12 14 16 18 200
0.5
1
1.5
2
2.5
time(s)
xc(t)
Fig. 12. xc × time (line) andxcestimated × time (dots).
0 2 4 6 8 10 12 14 16 18 200
0.5
1
1.5
2
2.5
time(s)
yc(t)
Fig. 13. yc × time (line) andycestimated × time (dots).
0 2 4 6 8 10 12 14 16 18 200
0.5
1
1.5
2
2.5
3
3.5
4
4.5
time(s)
thetac(t),rad
Fig. 14. θc × time (line) andθcestimated × time (dots).
0 0.5 1 1.5 2 2.50
0.5
1
1.5
2
2.5
xc(t)
yc(t)
Fig. 15. Robot trajectoryxc × yc (line) and estimated trajectory (dots).
0 2 4 6 8 10 12 14 16 18 20−2
−1
0
1
2
3
4
5
time(s)
v(t),m/s, omega(t),rad/s
Fig. 16. Robot linear (u1) - (line) and angular (u2) - (dots) speeds.
B. Path tracking
To verify the behavior of robot control when movingbetween several points a path tracking simulation was released.On this simulation result three main curves are observed:the system output (robot real position - continuous line), theestimated robot position (dashed line) and generated controlreference (circles). Figure 18 shows the robot displacement onx axis and figure 19 on y axis.The robot angle behavior indicates that the orientation of
the robot turned2πrad and turned back to initial orientationas shown on figure 20. It is possible to observe that differencebetween curves related with angle behavior is less than pathrelated. On figure 21 the robot displacement in Cartesian spaceis shown, where it is possible to observe that the estimated
0 2 4 6 8 10 12 14 16 18 20−400
−200
0
200
400
600
800
time(s)
npr(t), npl(t)
Fig. 17. Encoders reading simulation: right wheel (line) and left wheel (dots).
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position try to follow the control reference, otherwise it driftsfrom the real robot position. This fact is a characteristic of theodometry, being that better results on practical situations canbe achieved by means of calibration.Figure 22 demonstrates linear and angular speeds and
figure 23 the encoder readings from right and left wheels. Thecontrol signals generated by means of control law is shownon figure 24.
0 2 4 6 8 10 12 14 16 18 20−2
−1.5
−1
−0.5
0
0.5
1
1.5
2
2.5
time(s)
xc(t)
Fig. 18. xc×time (line) andxc estimated× time (dashed line) and controlreference (circles).
0 2 4 6 8 10 12 14 16 18 20−4
−3
−2
−1
0
1
2
3
4
time(s)
yc(t)
Fig. 19. yc×time (line) andycestimated×time (dashed line) and controlreference (circles).
0 2 4 6 8 10 12 14 16 18 20−1
0
1
2
3
4
5
6
7
time(s)
thetac(t),rad
Fig. 20. Robot angleθc (line) and estimated angle (dashed line) and controlreference (circles).
−2 −1.5 −1 −0.5 0 0.5 1 1.5 2 2.5−4
−3
−2
−1
0
1
2
3
4
xc(t)
yc(t)
Fig. 21. Robot pathxc × yc (line) and estimated robot path (dashed line)and control reference (circles).
0 2 4 6 8 10 12 14 16 18 20−5
0
5
10
15
20
25
30
35
time(s)
v(t),m/s, omega(t),rad/s
Fig. 22. Path speedsu1 (line) andu2 (circles).
0 2 4 6 8 10 12 14 16 18 20−80
−60
−40
−20
0
20
40
60
80
time(s)
npr(t), npl(t)
Fig. 23. Encoders reading path simulation: right wheel (line) and left wheel(circles).
0 2 4 6 8 10 12 14 16 18 20−5000
0
5000
10000
15000
20000
time(s)
nr,nl
Fig. 24. Control signals: right wheel (line) and left wheel (circles).
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V. CONCLUSION
This work showed the results of a robotic competition fromBrazilian Institution SENAI. The robot development allowedthe students team to get in touch with a lot of technicalknowledge from different areas like mechanical, electric,elec-tronic and pneumatics. Besides, the team could live differentsituations related with the competition, like psychologicalstress, teamwork and pro activity.On the competition first step an open loop guidance was
developed. In order to improve the control performance aclosed loop one is being developed to get better robot resultson the second step of the competition. Simulation resultshas shown that the robot will converge to a desired pointwhen following the control generated by control equations.Programming it on PLC allows the team to use a lot ofadvanced features of it.The proposed control law was validated by means of
mathematical model simulation. Future work can be developedby implementing the control law in the PLC and verifyingpractical results.
ACKNOWLEDGMENT
Authors would like to thank Fundação de Apoio à Pesquisado Estado do Rio Grande do Sul (FAPERGS) and Serviço Na-cional de Aprendizagem Industrial (SENAI) for the financialsupport.
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