Local Data Fusion Algorithm for Fire Detection through ... · Local Data Fusion Algorithm for Fire...
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Local Data Fusion Algorithm for Fire Detection through Mobile Robot Guilherme Freire Roberto UNESP - Universidade Estadual Paulista Sao Jose do Rio Preto, Sao Paulo Email:[email protected] Kalinka Castelo Branco USP - Universidade de Sao Paulo Sao Carlos, Sao Paulo Email: [email protected] J.M. Machado and A.R. Pinto UNESP - Universidade Estadual Paulista Sao Jose do Rio Preto, Sao Paulo Email:jmarcio,[email protected] Abstract—Multisensor data fusion is a technique that combines the readings of multiple sensors to detect some phenomenon. Data fusion applications are numerous and they can be used in smart buildings, environment monitoring, industry and defense applications. The main goal of multisensor data fusion is to minimize false alarms and maximize the probability of detection based on the detection of multiple sensors. In this paper a local data fusion algorithm based on luminosity, temperature and flame for fire detection is presented. The data fusion approach was embedded in a low cost mobile robot. The prototype test validation has indicated that our approach can detect fire occurrence. Moreover, the low cost project allow the development of robots that could be discarded in their fire detection missions. I. I NTRODUCTION Fire detection and prevention is an important issue in the preservation of forests, crops and buildings. In this context, several approaches have been proposed in the literature. Most of them are based on stationary wireless sensor net- works(WSN) [5], [8], [9], [11], however the main concern about these approaches is that a sensor will detect the fire occurrence, and it will be probably burnt after the detection. Moreover, a large number of sensors will be necessary to mon- itor a forest or a building. Another problem is the false alarm and loss of detection which are due to the low dependability of WSN nodes. Another research direction is the use of mobile robots [7] which can cover a larger area and few nodes (robots) are necessary due to their mobility. On the other hand, expensive robots are not suitable for fire detection missions due to the damage probability. Besides the detection method, fire detection techniques must rely on data fusion approaches [10]. Data fusion is a technology used in many applications like military and defense operations, smart buildings and manufac- turing processes [6]. In this context, multisensor data fusion techniques combining readings of multiple sensors like smoke, temperature or luminosity can overcome the high occurrence of false alarms in fire detection [4]. In this paper we present a local multisensor data fusion for fire detection based on a mobile robot. The main goal of the local data fusion algorithm is to present a low overhead due to the severe constraints of the used hardware. Moreover, the mobile robot is based on a low cost project. Therefore, it could be possible to assemble a large scale robot network for fire prevention in forests or facilities. Moreover, our local data fusion based on moving average filter is able to detect the occurrence of fire even when different kinds of fuels are used. The multisensor local data fusion was embedded in a Arduino 2009 board [1] and validated through prototype tests. The remainder of this paper is organized as follows: related fire detection approaches are presented in section II. The robot hardware and local data fusion approach is presented in section III. Section IV is dedicated to prototype tests, final remarks and future works are presented in section V. II. RELATED WORKS There are several research efforts addressing fire detection in the literature [2], [3], [5], [7]–[9], [11], [12]. Most of them are based on wireless sensor networks (WSN), whose main goal is to detect fire in forests [3], [5], [8], [9], [11], [12]. An approach based on statistical data modeling for WSN is presented in [2]. The technique is based on the Angstrom Index that is defined as: A I =0.05 * H - 0.1 * (T - 27) (1) where H is the relative humidity and T is the air temperature in Celsius degrees. Authors have used this index due to the fact that it is considered the most sensitive measure of fire risk [2]. However, the validation of the proposal was simulated in a simulator called Fire Dynamics Simulator that models the fire evolution in a building. The main purpose of these works was to reduce the messages which were sent to the sink node [2]. Finally, the main concern about this approach is the validation form which was based on a simulator, and the use of a fire index that is more suitable for the forest fire occurrence risk. The development of an intelligent security robot that detects fire occurrence in smart buildings is shown in [7]. Fire detec- tion module was based on three sensors: an ionization smoke sensor, a temperature semiconductor sensor and an ultraviolet flame detection sensor. These three sensors were combined in one module that was mounted in the front side of the security robot, the robot can also transmit the fire occurrence using a GSM modem. The data fusion algorithm was based on the probability of detection and on the probability of false alarm [7]. The main idea of the data fusion algorithm is trying to minimize the probability of false alarm through the multi- sensor data fusion. For example, some fire detection systems based on smoke sensor will detect a fire occurrence when a 978-1-4799-0597-3/13/$31.00 ©2013 IEEE
Transcript of Local Data Fusion Algorithm for Fire Detection through ... · Local Data Fusion Algorithm for Fire...
Local Data Fusion Algorithm for Fire Detectionthrough Mobile Robot
Guilherme Freire RobertoUNESP - Universidade Estadual Paulista
Sao Jose do Rio Preto, Sao PauloEmail:[email protected]
Kalinka Castelo BrancoUSP - Universidade de Sao Paulo
Sao Carlos, Sao PauloEmail: [email protected]
J.M. Machadoand A.R. Pinto
UNESP - Universidade Estadual PaulistaSao Jose do Rio Preto, Sao Paulo
Email:jmarcio,[email protected]
Abstract—Multisensor data fusion is a technique that combinesthe readings of multiple sensors to detect some phenomenon.Data fusion applications are numerous and they can be used insmart buildings, environment monitoring, industry and defenseapplications. The main goal of multisensor data fusion is tominimize false alarms and maximize the probability of detectionbased on the detection of multiple sensors. In this paper a localdata fusion algorithm based on luminosity, temperature andflame for fire detection is presented. The data fusion approachwas embedded in a low cost mobile robot. The prototypetest validation has indicated that our approach can detect fireoccurrence. Moreover, the low cost project allow the developmentof robots that could be discarded in their fire detection missions.
I. INTRODUCTION
Fire detection and prevention is an important issue in thepreservation of forests, crops and buildings. In this context,several approaches have been proposed in the literature.Most of them are based on stationary wireless sensor net-works(WSN) [5], [8], [9], [11], however the main concernabout these approaches is that a sensor will detect the fireoccurrence, and it will be probably burnt after the detection.Moreover, a large number of sensors will be necessary to mon-itor a forest or a building. Another problem is the false alarmand loss of detection which are due to the low dependabilityof WSN nodes.
Another research direction is the use of mobile robots [7]which can cover a larger area and few nodes (robots) arenecessary due to their mobility. On the other hand, expensiverobots are not suitable for fire detection missions due tothe damage probability. Besides the detection method, firedetection techniques must rely on data fusion approaches [10].Data fusion is a technology used in many applications likemilitary and defense operations, smart buildings and manufac-turing processes [6]. In this context, multisensor data fusiontechniques combining readings of multiple sensors like smoke,temperature or luminosity can overcome the high occurrenceof false alarms in fire detection [4].
In this paper we present a local multisensor data fusionfor fire detection based on a mobile robot. The main goal ofthe local data fusion algorithm is to present a low overheaddue to the severe constraints of the used hardware. Moreover,the mobile robot is based on a low cost project. Therefore,it could be possible to assemble a large scale robot networkfor fire prevention in forests or facilities. Moreover, our local
data fusion based on moving average filter is able to detectthe occurrence of fire even when different kinds of fuels areused. The multisensor local data fusion was embedded in aArduino 2009 board [1] and validated through prototype tests.
The remainder of this paper is organized as follows: relatedfire detection approaches are presented in section II. The robothardware and local data fusion approach is presented in sectionIII. Section IV is dedicated to prototype tests, final remarksand future works are presented in section V.
II. RELATED WORKS
There are several research efforts addressing fire detectionin the literature [2], [3], [5], [7]–[9], [11], [12]. Most of themare based on wireless sensor networks (WSN), whose maingoal is to detect fire in forests [3], [5], [8], [9], [11], [12].An approach based on statistical data modeling for WSN ispresented in [2]. The technique is based on the Angstrom Indexthat is defined as:
AI = 0.05 ∗H − 0.1 ∗ (T − 27) (1)
where H is the relative humidity and T is the air temperaturein Celsius degrees. Authors have used this index due to thefact that it is considered the most sensitive measure of fire risk[2]. However, the validation of the proposal was simulated in asimulator called Fire Dynamics Simulator that models the fireevolution in a building. The main purpose of these works wasto reduce the messages which were sent to the sink node [2].Finally, the main concern about this approach is the validationform which was based on a simulator, and the use of a fireindex that is more suitable for the forest fire occurrence risk.
The development of an intelligent security robot that detectsfire occurrence in smart buildings is shown in [7]. Fire detec-tion module was based on three sensors: an ionization smokesensor, a temperature semiconductor sensor and an ultravioletflame detection sensor. These three sensors were combined inone module that was mounted in the front side of the securityrobot, the robot can also transmit the fire occurrence usinga GSM modem. The data fusion algorithm was based on theprobability of detection and on the probability of false alarm[7]. The main idea of the data fusion algorithm is trying tominimize the probability of false alarm through the multi-sensor data fusion. For example, some fire detection systemsbased on smoke sensor will detect a fire occurrence when a
978-1-4799-0597-3/13/$31.00 ©2013 IEEE
smoker lights a cigarette, hence if other sensors are used thefalse alarm probability is minimized.
A forest fire detection system based on WSN is presented in[12]. This project was based on ZigBee WSN nodes, howeverthe authors did not present more details about the implemen-tation and validation of the technique. A multiparameter firedetection approach also based on WSN technology is shownin [8] where authors have tested the accuracy of smoke andtemperature sensors in the fire detection. The validation wasmade in their fire laboratory based on a standard cotton ropefire sensed by six smoke sensors. A neural network techniquefor real time fire detection based on WSN is discussed in [11],the neural network was validated through simulation
Finally, a comparison between our fire detection approachand related works is presented in Table I. It is possible to noticethat our approach shows more similarities when comparedwith [7]. Based on the fast growth of the computational poweravailable in low power microprocessors and microcontrollers,the miniaturization of electronic components, and improve-ments in servos, sensors and actuators, it is now possibleto develop efficient algorithms to provide sensor functionsthat are capable of performing fire detection at a very lowcost in comparison with the ones presented in the literature.Therefore, the low cost robot project and the low overhead ofthe data fusion approach(that was able to be embedded in aconstrained embedded system) are the main advantages of ourproposal.
III. FIRE DETECTION APPROACH - MOBILE ROBOTPLATFORM AND LOCAL DATA FUSION FIRE DETECTOR
In this section we will present the algorithm used to detectfire and the robot prototype assembled to test the fire detectionapproach.
A. Hardware Description
In this section the mobile robot for fire detection is pre-sented. The robot is based on a tread mill, this characteristicis necessary due to the inhospitable environments where therobot will be used. The robot has 19x11 cm as dimensionsand it is based on a TAMIYA DC engine that is controlledby a Ardumoto shield and a 3,7 V battery. The Ardumotoshield is based on a H-bridge L298 (which can control two2A DC motors) and is attached to a Arduino 2009 board(an Atmel AVR embedded system). The details of the mobilerobot for fire detection are presented in Fig. 1. Furthermore aservo motor is used to move the platform of sensors (that iscomposed by temperature, luminosity and flame detectors).
The terrestrial robot that was employed in our tests is basedon five modules described below:
1) Intelligence: the intelligence module is controled by aArduino 2009 board. This module is responsible for thelocomotion algorithms and for the control of data fusionapproaches.
2) Locomotion: this module is based on DC motors thatpropels a tread mill platform, thus it is possible to
TABLE ICOMPARISON BETWEEN APPROACHES
Project Sensors RoboticPlatformBased
Servo Detection Environment
[5] Humidity No No Outdoor
[12] - No No Both
[8] Smoke,Temper-ature
No No Both
[7] Flame,Smoke,Temper-ature,Infrared
Yes No Indoor
[2] Humidity,Temper-ature
No No Both
[3] Humidity,Temper-ature,Smoke
No No Outdoor
[11] Humidity,Temper-ature,Smoke,WindSpeed
No No Outdoor
Proposed Flame,Temper-ature,Lumi-nosity
Yes Yes Both
Fig. 1. Mobile Robot for Fire Detection
explore dangerous environments like disaster areas orindoor and outdoor military tactical missions.
3) Communication: the communication module is basedon the IEEE 802.15.4 standard (Xbee Module). IEEE802.15.4 is a low power and low data rate protocolfor wireless networks, and it is suitable for automationprojects in which one of the main goals is to save energy[13].
4) Sensors: the sensing module is composed by analogicaland/or digital sensors. The fire detection robot hastemperature, luminosity and flame sensors. However,
the robot can be easily modified in order to be usedin environmental monitoring or tactical missions, whereother kinds of sensors are necessary.
5) Actuators: this module is basically composed basicallyby the servo motor that controls the fire detection sensorplatform. Other projects could include joysticks for therobot control or robotic arms to manipulate dangerousobjects.
Fig. 2. Detail of Robot Modules
The robot modules presented above were projected for ageneric mobile robot and the sensor, actuators and boards usedin the robot for fire detection can be changed for other projects.Moreover, the project was designed in order to achieve a lowcost robot platform that could be discarded or lost during itsmission.
B. Data Fusion for Fire Detection
The data fusion algorithm used in our fire detection ap-proach is based on a simple moving average filter. Movingaverage is commonly used in stock markets to predict thebehavior of a stock (if it will increase or decrease its price).The moving average of a point P1 is calculated based on theaverage of a set of values collected in a interval [X1,XN ], P2is based on the average of the interval [X2,XN+1]. The mainidea of our local data fusion algorithm is to detect the trendof some scalar that is sensed based on the same idea that isused in stock market, in this case we have tested temperature,flame and luminosity sensors. The algorithm for fire detectionis presented in Fig. 3. It is possible to notice that the first stepis the movement of the platform of sensors, then sensors areread and then the moving average filter detects if there area fire occurrence. Moreover, the moving average algorithm isshown in Algorithm 1, it basically receives the period andsensor reading then it decides if the current sensor reading isa fire occurence or not.
IV. PROTOTYPE TESTS
The validation of the proposed local data fusion approachwas based on prototype tests. We have firstly executed firedetection tests with alcohol flame (Fig. 4) and paper flame(Fig. 5). Based on these tests we have detected that each type
Fig. 3. Fire Detection Algorithm
Algorithm 1 Moving Average Filter for Fire DetectionRequire: MVA⇐MovingAveragePeriod
sensorV alue⇐ sensorReadingMA⇐ calculateMovingAverage(MVA,SensorReading)if SensorV alue > MA then
FireAlarm()end if
of fuel (paper or alcohol) produced distinct values of flames,temperature and luminosity. This behavior is the main reasonto use a moving average filter that just detects the variation ofthe sensed values besides their thresholds. After the first roundof tests, we have divided our testbed in three steps: tests todetect the temperature and luminosity of fire, tests to validatethe flame sensor behavior in different distances from the flame,and tests to detect the best moving average parameters (numberof samples).
A. Moving Average Parameters
The purpose of this testbed is to detect the best movingaverage period that minimizes false alarms and that coulddetect fire occurrences. We had made tests of 60 seconds, after30 seconds a flame was initiated at 30 centimeters from therobot. We have tested periods of 10, 20 and 30 seconds ofmoving averages. Figures 6, 7 and 8 show the behaviour ofour filter. The choices of 30 and 20 seconds of period didnot present a good performance as we can notice in Figures
Fig. 4. Detail of a indoor test based on a alcohol flame
Fig. 5. Detail of a outdoor test
7 and 8, and these moving average periods did not properlydetect the flame occurrence. Finally we have concluded that 10seconds of period is a more suitable choice for fire detection.
Fig. 6. Tests of the flame detector - 10 seconds of interval.
B. Distance Test
The distance testbed was executed to determine the idealposition from the flame where the flame sensor must be tominimize false alarms and to maximize the probability ofdetection. Thus Figures 9, 10 and 11 show the flame detectionby our prototype. The maximum distance at which flamesensor could sense was 1 meter and the minimum 10 cm.
Fig. 7. Tests of the flame detector - 20 seconds of interval.
Fig. 8. Tests of the flame detector - 30 seconds of interval.
However, it is possible to notice that 1 meter is the idealdistance of detection. This is an important information thatcan be used to prevent robot damage.
Fig. 9. Tests of the flame detector - 10 cm from the flame.
C. Servo Position
The servo position testbed was executed to determine themaximum angle which the flame sensor must be positioned todetect the flame occurence. Moreover, this testbed can provethat the mobile sensor platform is able to detect fire in a wideangle. Figures 12 and 13 show that even when the sensorplatform is in motion, the fire occurrence is still detect by ourapproach.
Fig. 10. Tests of the flame detector - 50 cm from the flame.
Fig. 11. Tests of the flame detector - 1 m from the flame
Fig. 12. Tests of the flame detector with the servo motor in motion - 10cmfrom the flame
Fig. 13. Tests of the flame detector with the servo motor in motion - 25cmfrom the flame
D. Threshold of Temperature and Luminosity
This testbed was modelled to detect the best thresholds of aflame temperature and luminosity. The first step was to test thenormal temperature and luminosity in an environment withoutfire occurrence, according to Figures 14 and 15 the luminosityvalues of a normal room are between 700 and 830 lux andbetween 20 and 35 celsius degrees for temperature. Thus, weused the threshold values 900 lux for the luminosity sensorand 40 celsius degree for the temperature sensor. However, it isimportant to notice that our approach also relies on the movingaverage filter and on a multisensor technique. Therefore, if ahigh temperature occurs and there is no fire then both the flameand luminosity sensors will eliminate the false alarm.
Fig. 14. Tests of temperature threshold
Fig. 15. Tests of luminosity threshold
V. FINAL REMARKS AND FUTURE WORKS
In this paper we presented a local data fusion for fire detec-tion through mobile robots focused not only in the algorithmprecision, but also in a low cost project. The data fusion basedon a moving average technique is considered a multisensorapproach that is based on flame detection and temperatureand luminosity measurements. Prototype tests have shown thatour mobile robot is able to detect fire occurrence even whendifferent kinds of fuels are used. Moreover, the low cost robotproject allows the deployment of a large scale fire detectionrobot network, what is very difficult and expensive based onthe existing robots. As future research directions, we intend to
develop a network of robots and to execute validation tests ina more realistic scenario.
ACKNOWLEDGMENT
The authors acknowledge the support granted by CNPq andFAPESP to the INCT-SEC (National Institute of Science andTechnology - Critical Embedded Systems - Brazil), processes573963/2008-9 and 0 8/57870-9.
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