Wireless Local Area Network Based Fire Monitoring Robot Design Rong Gao 1, a, Qisheng Wu 2,b and Lan Bai 3,c

Transcription

1 Applied Mechanics and Materials Online: ISSN: , Vols , pp doi: / Trans Tech Publications, Switzerland Wireless Local Area Network Based Fire Monitoring Robot Design Rong Gao 1, a, Qisheng Wu 2,b and Lan Bai 3,c School of Electronic and Control Engineering Chang an University, Xi an , China a ggidea@st.chd.edu.cn, b qshwu@chd.edu.cn, c bailan @163.com Keywords: fire monitoring, wireless, LWIR, DM642, robot. Abstract. A tms320dm642 and wireless fidelity based fire monitoring robot is designed. Flame features, both static and dynamic detecting algorithm, combine with long wave infrared (LWIR) is equipped to achieve the goal of monitoring fire. When the suspected fire event happens, Fire warning message will be sent to remote terminal through the wireless LAN automatically. Infrared image of the fire can be transmitted through the wireless network under the control of remote terminal. As LWIR camera can even look through the dense smoke of fire, fire source will be located accurately, rescuing and fire fighting work will carry on better and with less injury. 1 Introduction In the purpose of detecting fire, many early fire-detection techniques have been explored and most of them are based on particle sampling, temperature sampling, relative humidity sampling, air transparency testing, in addition to the traditional ultraviolet and infrared fire detectors [1]. However, such sensors can not provide further information about fire source position, fire size and spread speed, further more the effective detection distance is quite short [1]. In recent years, mobile fire monitoring method received widespread concerns. For example: Fire Searcher is a robot designed for usage in the extreme conditions such as high temperatures or poisonous gases. It monitors the internal situation of a fire site and victims and sends back crucial information to its operator at a remote site [2]. Meanwhile, Jet Fighter is a type of Autonomous Fire Fighting Mobile Platform that is introduced by Tokyo Fire Department. It can be operated and controlled by remote user and has the ability to extinguish flame after locating the source of fire. It is equipped with a monitoring system and operates through a wireless communication system. An obstacles avoidance system is embedded into its autonomous navigation system [3]. A mobile platform can also be built around a wireless sensor network for its intra-system communication, for example using zigbee wireless communication modules. It allows for relatively huge data transfer such as video and audio from the robot to a remote control centre. It also allows for tracking the robot s position through the signal strength of the wireless sensor network [4]. Thanks to the improvement of resolution, speed and sensitivity of IR imaging, this newer type of imagery is started to be explored as a way to improve the object detection and tracking performance. IR imaging is already used successfully in many video surveillance applications, e.g. traffic safety, airport security and material inspection. Recently, IR video based flame detection is also gaining importance [5]. The paper presents the design of fire monitoring robot uses long wave infrared video combined with flame features detecting algorithms to judge if there is a fire. By the wireless LAN data transmitting technology, the robot can be controlled remotely by the operator and the real-time infrared image of fire area can also be obtained. The following chapters introduce some key characteristics of the robot. 2 Global description of the methodology 2.1 Flame Detection Unlike commonly used video cameras that record reflected light, a long-wave (8 12 μm) IR sensor records electromagnetic radiations emitted by objects in the scene. As such, the LWIR (thermal) images represent temperature: the warmer an object is, the brighter it appears on the images [5]. Tms320dm642 processes the IR video filmed by the Xenics Gobi-384 LWIR camera in its memory, All rights reserved. No part of contents of this paper may be reproduced or transmitted in any form or by any means without the written permission of Trans Tech Publications, (ID: , Pennsylvania State University, University Park, USA-09/05/16,18:25:21)

2 Applied Mechanics and Materials Vols based on the YCrCb color space. Y components of the IR image indicate the temperature distribution of the target, while the CrCb components are neglected. Y value of each pixel corresponds to a fixed temperature, which is calibrated inner in the LWIR camera. Firstly, high temperature pixel is extracted from the IR image by setting a temperature threshold as follows: In Eq. 1, represent the intensity of a pixel, is the high temperature threshold. Now all the suspicious pixels are extracted from IR image. In order to exclude the false alarm caused by other high temperature objects, flame dynamic feature detecting algorithm is applied. Flame has obvious flicker features because the energy release during combustion progress is not stable [6]. In Eq. 2, where represent the number of high The flicker feature of fire causes the area of high temperature pixel in IR images change constantly, so that the statistic value of high temperature pixel changes in the flame infrared image, can effectively reflect the flame flicker features, which will distinguish flame and other stable high temperature object or mobile high temperature object effectively. temperature pixels in the i th frame of an IR image sequence. Ac is the accumulation of changes, which is reset to 0 every 10 frame of IR pictures. While is a threshold to judge whether it is a fire video in Eq. 3. Both and should be set based on the experiment result. (2) (1) (3) 2.2 Wireless LAN and Navigation System WiFi (i.e., IEEE based wireless local area network) is taking the leading role for the wireless communication due to its cheap integration cost and almost free-of-charge networking availability [7]. The network architecture of our system is shown in Fg.1. A WLAN card is connected to the MAC port of tms320dm642, and meanwhile, AP note point is equipped to connect the WLAN card and the remote terminal. Sets of infrared (IR) sensors were used to implement the line following feature on the platform. Each set of IR sensors are arranged side by side for both IR emitter and IR receiver. The different level of IR reflection created by the black lines and white lines become the principle of working for the line detector. Different voltage levels will be generated depending on the output from the IR receiver, whether there is any reflected light from the reflective lines. Four groups of IR sensors are arranged at the head of our robot, voltages of each IR receiver are converting to 8bit digital value by the microcontroller s inner analog to digital converter. Digital filter arithmetic is applied to keep the reliability of data. In order to overcome the parameter difference between each group of sensor, and enhance the adaptability of the robot for different road and illumination environment, normalized processing is applied, which is shown in Eq. 4.. (4) is the normalized value of each sensor, is the original value, is the value range of each sensor at the current road lighting environment. Subscript i is number of each IR sensor unit (i=1,2,3,4). The motor driver of robot is controlled by the duty factor, which is outputted by the microcontroller s PWM unit, while duty factor is generated through the PID regulator which is calculated by the Micro controller. The difference equation of discrete PID regulator is shown in Eq. 5. (5) are the proportional, integral and differential coefficients of discrete PID regulator; is the deviation variable of each sensor, which quantified the driving deviation according to the guide line; is the PWM control duty factor. (6)

3 High temperature pixels Accumulate of changes 584 Advances in Mechatronics and Control Engineering are the deviation variable of four groups IR sensors. Parameters of the PID regulator are set through experiment. Finally smooth and stable action function of the robot is realized. 3 Results & Discussions In order to verify the proposed fire monitoring robot, we performed several real-life fire and non-fire experiments. The multi-modal sequences were acquired by a Xenics Gobi-384 LWIR camera which works in the 8 14 μm spectral range. The Gobi thermal imager has a resolution of pixels and a frame rate of fps. Test sequence,consists of 10 frames IR image are extracted from the IR video at the rate of a frame per 200 milliseconds. Fig.1 shows several different IR images extracted from fire video(a,b) and interfering video(c,d), the result of extracting high temperature pixels from fire and interfering video frames: fire(e,f); interfering target(g,h). The threshold of high temperature pixels extraction was set to 140 degrees Celsius. (a) (b) (c) (d) (e) (f) (g) (h) Fig. 1 IR image of fire and non-fire (a,b,c,d), high temperature pixel extraction (e,f,g,h) As the results in Fig. 2, high temperature pixels in each frame(a); Accumulate of pixels changes between adjacent frames, recounted every 10 frames(b) show, the number of high temperature pixels in the fire sequence fluctuates a lot, while the number of high temperature pixels in the interfering video sequence is approximate at the same level. Fig. 2(b) show a fire alert signal was generated, since the accumulation of high temperature pixel changes between adjacent frames became greater than threshold 1000 at the 7th frame of the fire sequence. On the other hand, the change accumulation of non-fire sequence grows slowly and that indicates it is not a fire sequence. Some pictures of our wireless-based fire monitoring platform is shown in Fig fire non-fire fire non-fire Alert (a) Frame number 200 (b) Frame number Fig. 2 Calculate result of fire and non-fire Sequence

4 Applied Mechanics and Materials Vols Fig. 3 Some pictures of wireless-based fire monitoring platform 4 Conclusions The developed wireless local area network based fire monitoring robot, realized the required function perfectly with very low rate of false alarm. Based on the findings, integrating all the hardware such as motor driver circuitry, LDR sensors, WIFI module and LWIR camera, the expected patrolling and fire monitoring tasks are possible to be carried out and executed with minimum level of error. By deploying the robot to monitor for hazardous site via patrolling process, it aids to ensure the fire-fighters safety in fire fighting tasks. Acknowledgements Thanks for the funding and support of follows projects: Basic Research Project of Ministry of Transport of People s Republic of China( ),Research on the key technology of WSN and image based underground station fire perception(2011k06-31),central Universities special funds for basic research and Operating Expenses Projects of Chang an University (Innovation Team CHD2011TD018),Major science and technology Project of The 12th Five-Year Plan for national economic and social development of the People's Republic of China ( ); Thanks the valuable comments and suggestions of the reviewers of this paper; Thanks all authors in the literature references; Thanks for the training and education of my mother school Chang an University. References [1] Thou-Ho Chen, Yen-Hui Yin, Shi-Feng Huang and Yan-Ting Ye.: The smoke detection for early fire-alarming system based on video processing. In: IEEE 2006 International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH MSP ' 2006).Washington, DC, USA : IEEE Computer Society Press, 2006: [2] P.H. Chang and Y.H. Kang, et al., Control Architecture Design for Fire Searching Robot using Task Oriented Design Methodology, SICE-ICASE 2006, Oct [3] Verstockt, S., Dekeerschieter, R., Vanoosthuise, A., Merci, B., Sette, B., Lambert, P. & Van de Walle, R., Video fire detection using non-visible light, 6th International seminar on Fire and Explosion Hazards (FEH-6), [4] Kuo L. Su; Automatic Fire Detection System Using Adaptive Fusion Algorithm for Fire Fighting Robot, Systems, Man, and Cybernetics, IEEE International Conference, 8-11 October 2006, Pages: [5] Steven Verstockt Chris Poppe Sofie Van Hoecke Charles Hollemeersch Bart Merci Bart Sette Peter Lambert Rik Van de Walle; Silhouette-based multi-sensor smoke detection, Machine Vision and Applications 5 July [6] Calderara, S., Piccinini, P., Cucchiara, R.: Vision based smoke detection system using image energy and color information. Mach. Vis. Appl., 22(4), (2011). [7] Owrutsky, J.C., Steinhurst,D.A., Minor,C.P., Rose-Pehrsson, S.L., Williams, F.W., Gottuk, D.T.: Long wavelength video detection of fire in Ship compartments. Fire Saf. J. 41, (2006).

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