Sniffing a fire: experiments in a reduced scale scenario - CiteSeerX

Sniffing a fire: experiments in a reduced scale scenario Pedro Oliveira, Lino Marques and An´ıbal T. de Almeida Institute for Systems and Robotics University of Coimbra 3030-290 Coimbra, Portugal {poliveira,lino,adealmeida}@isr.uc.pt Abstract— This paper addresses the searching of indoor fires with mobile robots using olfactory information. An experimental environment and devices necessary to study maze searching algorithms using olfaction are described. A maze solving algorithm, taking into account the smoke direction is proposed and validated in a simulated scenario. A simulated Khepera III mobile robot equipped with an olfaction system and an infrared array is used in a set of trials searching small simulated fires inside a weakly ventilated maze with 4x3 square meters.

algorithms for searching fires in a maze. A modified Bug2 algorithm [5] that takes into account the olfactory information to go in a more directive way across the maze until a fire is described and simulated results of running this algorithm inside a maze are presented. Section V presents the main conclusions of this work.

I. I NTRODUCTION

Several algorithms have been proposed to solve unknown mazes using different types of sensory information [5]. Most of these algorithms assume that the robot knows the position of the target and its own position. While other algorithms, for example the ones used in micromouse contests [11], assume that the target localization is not known beforehand, but the robot carries some kind of sensor (usually a vision sensor) that allows detecting the target when the robot is sufficiently closer and with the right orientation. To solve this type of problem, the robot needs to use some kind of exploration algorithm moving across the maze until its sensors can detect the target direction. Without sensors providing early guidance about the target direction, a maze exploration process can be very time consuming, particularly for mission critical situations, like searching for a fire. When the target is an odour source and some airflow exists in the environment, the odour plume generated by the target can be used as a guidance cue about the target localization, allowing a much faster maze exploration. To the best of the authors’ knowledge, no algorithm has yet tried to integrate airflow and olfactory information to help solving a maze until the odour source. The tracking of odour plumes in atmospheric environments has been studied by several researchers, namely by Ishida [4], Russell [9], and Marques [7]. This work expands the previous works in several aspects, namely by the utilization of olfactory information as a cue to guide a robot across a maze, and by the utilization of olfaction in robots to improve their capabilities for mission critical applications, like searching indoor fires.

Any fire and in particular industrial fires inside a warehouse release smoke composed by a mesh of chemical vapours that spreads rapidly by convection and advection forces through the entrances of the building. This smoke is usually detected by ionization detectors or by the occlusion measured through optical beams providing alarms about a possible fire situation. Heat detectors can also be used as fire detectors, but these systems tend to only detect fires when they are already in an advanced state. The main problems of this type of fire detectors are being fixed in specific positions, providing late detections when a fire starts far away from the sensor, and relying on a single detection principle, being highly error sensitive and providing a high false alarm rate. For example, it was reported that all actual fires inside U. S. aircraft cargo compartments between 1974 and 1999 have been detected by the onboard fire detectors, but the false alarm rates where higher than 99% [1]. It has been shown that the utilization of multiple sensing principles, particularly with the inclusion of gas sensors to detect the vapours typically produced by fires, highly decreases the false alarm rate [10], [8], [2]. The other problem described, reducing the distance from the sensor to the fire, can be addressed with mobile robots equipped with very sensitive gas sensing systems that able to them to sense potential fires from a distant place downwind and move upwind until the proximity of the odour source in order to be sure if a fire or not. Such system could improve the safety inside large buildings and industrial facilities and have a huge economic impact by the reduction of false alarm rates and by the early detection of serious fires. This type of autonomous system doesn’t exist yet and some steps towards its implementation are addressed in this paper. Section II describes a Khepera III robot and the sensing systems integrated in the robot to detect fires, namely: an olfactory system composed by an electronic nose and a directional anemometer, and a thermal sensing array. This mobile robot is being used in experiments to search fictitious fires inside an unknown maze. Section IV describes three

A. Related works

II. E XPERIMENTAL ENVIRONMENT A. Reduced-scale environment A reduced scale experimental environment was set-up in order to allow identifying some relevant environmental characteristics for searching a fire source inside an enclosed environment using olfaction and to test olfactory-based searching algorithms in these conditions. The characteristics captured were a distribution of the airflow velocity and the smoke

chemical concentration. This data can be used to experiment several fire searching algorithms in simulation scenarios. The testing environment constructed to experiment fire search algorithms is composed by an enclosed area with 4 × 3 square meters and 0.5 meters height. An acrylic glass ceiling closes the upper part of the testing area and a modular MDF1 walls can be placed in the interior in order to implement different mazes. A set of controllable ventilators can be installed in an exterior wall extract a controllable amounts of air flowing through the maze from any entrance until the extraction ventilators (see Figure 1). Fig. 2. Picture showing the ventilator system used to control the airflow inside the small-scale experimental environment.

0.1 m/s [6]. The gas concentration of the vapours released by the fire inside the environment was continuously monitored with an array of twelve Figaro TGS2600 gas sensors [3] placed near the top of the environment (about 0.5 m height) in a regular square grid spaced with 1 meter from each orher (see Figure 1). The TGS2600 gas sensor is designed for the detection of general air contaminants, being able to detect few parts per million (ppm) of the vapours released by the simulated fires. The output from these sensors was acquired by a sensor network made with two Microchip PIC18F4431 microcontrollers interfacing to a PC through an RS485 shared bus, as represented in Figure 1. B. Robot platform

Fig. 1. Representation of the small scale scenario used to simulate fires. The localization of the nodes of a sensor network employed to monitor the experiments is also represented in the picture.

Four 12 cm diameter computer fans attached to each other in a square arrangement, as shown in Figure 2. Each one of these fans could extract air with a flow ranging from 0 to 1500 l pm. Fires are simulated in this environment by the burning of small pieces of paper or using a candle inside the testing area (usually near the entrance opening). The airflow inside the environment was characterized by an array of thermal anemometers with a sensitivity of about 1 Medium

Density Fiberboard

A Khepera III mobile robot was prepared to be used as a testing platform for the fire searching algorithms using olfaction. The Khepera III is a small differential drive robot with five sonars and eleven infra-red sensors to measure the distance to the surrounding obstacles. This robot was upgraded with a four gas sensing elements electronic nose and an eight elements infrared thermal sensing array (see Figure 3). The thermal sensing array, represented in Figure 4, allows detecting the fire source by the heat released by the flames when the robot has line-of-sight with the fire. This sensor is based on the thermopile array TPA81 which can detect infrared radiation in the 2 − 22 µm wavelength range, being able to easily detect a candle flame by its heat at a range up to 2 metres away. The typical field of view of a TPA81 is 41◦ by 6◦ which corresponds to a field-of-view of 5.12◦ by 6◦ for each of its eight pixels. All of these sensing modules interface with the robot through I 2C, that provides the environmental temperature and the radiant temperature measured by each of its pixels. The electronic nose detects the fire whenever the released vapours are carried by the airflow and come into contact with the robot (i.e., when the robot is downwind the fire inside the smoke plume). The eNose employed is composed by an array of four Figaro TGS26XX gas sensors, the respective signal

Fig. 5. Gas concentration measured by sensors A to D after burning a small paper in the entrance of the experimental area. Fig. 3.

Picture showing a Khepera III robot with an olfactory system.

Fig. 4.

Picture of a TPA81 infrared thermal array.

conditioning electronics, local processing by means of a Microchip PIC18F2321 microcontroller and I 2C communication interfacing. A ThermalSkin array can be added to the system to provide estimates of airflow intensity and direction, but due to problems arising when too many devices were sharing the robot I 2C bus, instead of using this sensing system, an offline characterization of the airflow inside the environment was saved in the robot memory.

the higher fluctuation measured by the sensors closer to the fire (A and B). As the smoke moves away from the fire, the plume becomes more homogeneous and the output from sensors closer to the exit area becomes smoother (see Figure 7 for a detail).

III. P LUME OF SMOKE

IV. F IRE S EARCHING A LGORITHM

To analyse how chemicals spread inside the small scale environment, fires were simulated and the concentration of the chemical volatiles released were gathered by the previously described sensor network. An example of such experiment can be seen by the concentration measures represented in Figure 5. In order to not overwhelm the representation, only the output measured by four sensors localized in the plume main way to the exiting ventilators are shown (sensors A to D in Figure 1). The motion of the smoke can be clearly seen from the response delay of each of the four sensors (see Figure 6). Another characteristic that can be observed from the data is

Three algorithms for searching fire sources in a maze were tested in a Player/Stage simulated environment. To test these algorithms, a model of the environment represented in Figure 1 was built and a model of a Khepera III robot, with its sonars and infrared range sensors, was also created. The Khepera III model was obtained modifying the model of a general differential drive platform. All algorithms use the thermal detection provided by the infrared array module to detect a fire source. The model of this sensing module was created by the modification of a general video camera model. The third tested algorithm uses olfaction to

Fig. 6.

Detail of the concentration levels during the beginning phase.

Fig. 9.

Simulation results with a wall-following algorithm.

Fig. 10.

Simulation results with a modified Bug2 algorithm.

Fig. 7. Detail of the concentration fluctuations measured in the middle of the experiment.

Start Explore Maze

___ Fire

IR_cue

Move to Heat Fire Found Fig. 8. State diagram for searching a fire in a maze using an IR thermal array to detect the fire.

improve the fire searching process. In order to simulate this sense, a spatiotemporal model of the smoke distribution and airflow intensity and direction, based on the data gathered by the previously described sensor network, was included in the Player/Stage environment. This model was used to provide olfactory sensing data to the simulated robot. The first two fire searching algorithms tested in this work follow the general state diagram represented in Figure 8. In the first algorithm, the maze is explored following its walls until eventually reaching a place where the fire can be detected by the IR array. If an already explored localization is reached, the robot should leave the wall until finding an obstacle with a new wall to explore. The Tremaux algorithm was implemented to keep track of the areas already explored. An example of running such strategy in the described environment can be seen in Figure 9.

The second algorithm is a Bug2 algorithm modified to explore an area without knowing in advance the target localization. Bug2 algorithm assumes that the target localization is known in advance and considers an imaginary line (m-line) connecting the start and target positions. In this algorithm the robot tries to follow the m-line until the target. If this line intercepts an obstacle, the robot circumvents the obstacle boundary (follows the walls) until reaching the m-line again and proceeding its way to the target. Although this algorithm can not be directly employed to search fires in a maze, since the fire localization is not known in advance, it can be adapted to the “Explore Maze” state of the previous state diagram assigning random target positions that will be changed when the robot reaches the given target or when it concludes that the target is unreachable. Figure 10 shows an example using this algorithm complemented with the ability to switch the searching state when the IR module detects a fire. In the third proposed algorithm, called here Ol f actoryBug, the m-line concept is replaced by the centreline of the plume of smoke (let it be p-line). Of course that this line is fuzzy and difficult to define, but if the following assumptions are made, it will become easier to estimate its approximate localization: • The environment is an indoor maze composed mostly by

Start Explore Maze

_____ Plume

Olf_cue

Follow Plume

___ Fire

IR_cue

Move to Heat Fire Found Fig. 11.

• •

State diagram of olfactory-based searching algorithm.

rooms and corridors with a small airflow passing through the fire localization; the maze is composed by isolated walls, so no air can flow through them; the fire is supposed to be in some detectable obstacle.

If the previous assumptions about the environment are valid, then the following assumptions about the p-line can be made: • •

The p-line direction should be parallel to the airflow direction; the p-line should be close to the middle of a corridor, or in the case of a large space, approximately in the middle of the boundaries of the smoke plume.

If olfaction is included in the fire searching algorithm, the previous state diagram should be augmented to include the motion across the p-line (the “Plume Following” state in Figure 11). In this case, the fire detection can also be more robust since two criteria can be used in the classification process: heat and chemical vapours release from a fire, e.g. a common heat source, like a radiator or an incandescent lamp will not be wrongly classified as a fire. The fire declaration can be as follows: if the robot is tracking a plume of smoke and detects a source of heat, it moves to that source. If the gas concentration and heat intensity are higher than pre-specified thresholds when the robot becomes closer than a pre-specified distance threshold, then the robot declares a fire, otherwise it is just a heat source and not a fire and the robot should proceed with the searching process. An trajectory example of a simulated Khepera III employing

Fig. 12.

Picture of the smoke tracking simulation with olfactory system.

this strategy is shown in Figure 12. In that Figure, the airflow intensity vectors along the robot trajectory are also represented (blue arrows). These vectors are continuous estimates obtained from an experimental characterization made with discrete measurements inside the real environment. It can be observed that this algorithm provides a much more directive behaviour while searching fires in a maze. These are preliminary results that need deeper study and testing. V. C ONCLUSIONS This paper described preliminary works carried-out to search fire spots inside enclosed mazes using olfaction. An experimental setup to test this type of works was constructed and is described, including an instrumented maze and a robot olfactory system. The olfactory detection of the vapours released by a fire was verified in the constructed experimental environment. A higher fluctuation of the gas concentration with the proximity to the fire spots was observed in these tests. A maze searching Bug-inspired algorithm is proposed and simulated with the Player/Stage environment. Preliminary simulations show more directive maze transversal and faster finding of fire sources, but these achievements need to be confirmed by deeper simulations and by experimental tests with real robots. ACKNOWLEDGMENTS This work was partially supported by the Portuguese Science and Technology Foundation (FCT/MCTES) by project RoboNose, contract POSI/SRI/48075/2002 and by project GUARDIANS contract FP6-IST-045269. R EFERENCES [1] D. Blake. Aircraft cargo compartment smoke detector alarm incidents on us-registered aircraft, 1974-1999. Technical Report TN00/29, DOT/FAA/AR, 2000. [2] Shin-Juh Chen, David C. Hovde, Kristen A. Peterson, and Andr´e W. Marshall. Fire detection using smoke and gas sensors. Fire Safety Journal, 42(8):507–515, 2007. [3] Figaro Inc. Tgs 2600 data sheet. Technical report, Figaro Inc., http://www.figarosensor.com, 2001.

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