A Biologically-Inspired Algorithm for Gas/Odor Source Localization in an Indoor Environment with no Strong Airflow: First Experimental Results Gabriele Ferri, Emanuele Caselli
Virgilio Mattoli, Alessio Mondini, Barbara Mazzolai, Paolo Dario
BioRobotics Engineering School IMT Lucca Institute for Advanced Studies Via S. Micheletto 3, 55110 Lucca, Italy Email:
[email protected]
CRIM Laboratory Scuola Superiore Sant’Anna Viale Rinaldo Piaggio 34, 56025 Pontedera (Pisa), Italy Email:
[email protected]
.Abstract – This paper describes the design of a biologicallyinspired SPIRAL (Searching Pollutant Iterative Rounding ALgorithm) algorithm, for the localization of a gas source in an indoor environment with no strong airflow. Such environment shows a few aspects that make the issue of finding an odor source much harder than in the presence of a strong wind. In fact, in a windless room, the gas diffusion mechanism is strongly perturbed by convection flows and turbulence, which make gas distribution very complex. Moreover, the algorithms used for gas source localization with no predominant airflow cannot exploit the upwind surge movement, usually exploited in traditional algorithms. We discuss the results of SPIRAL implemented on a robotic platform, called MOMO (Multirobots for Odor MOnitoring). Tests show that SPIRAL algorithm localizes a gas source with good results, by using only one gas sensor, and without either referring to anemometers or to any information about wind distribution.
Index Terms— indoor monitoring, gas source localization, bio-inspired algorithm.
I. INTRODUCTION
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HIS paper describes the design and implementation of a biologically-inspired algorithm on a robotic platform, aimed to localize a gas source in an indoor environment with no strong and constant airflow, that represents in a very accurate way a real indoor area, for example a factory or a storehouse. Gas source localization involves the challenge to rapidly localize an emitter source ejecting a chemical agent. Improvements in this fast localization system could open up new horizons in robotics. Autonomous robots can be used to localize a gas/odor source, to minimize the exposure of human beings to dangerous pollutants. These robotic automatic systems can have many applications in real environments, both in outdoor and indoor areas. In outdoor areas, they can be used to monitor a poisoned area; to find hidden explosives or narcotics; and, in addition, to perform rescuing tasks after accidents or natural disasters, because they could find survivors, detected as carbon
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dioxide sources. In indoor environments, they could be used to detect pipeline leakage in a domestic or industrial environment, to prevent carbon monoxide poisoning, caused by fire or inadequate ventilation of obstructed stoves, just to give a few examples. Researchers started to face the gas source tracing issue using techniques based on gradient-following [1]. Even if gradient-following techniques can improve the tracing performance on average [2], they are liable to errors due to the non smooth and multimodal gas distribution in real environment. Moreover, the current gas sensors technology is very far to be considered mature. In fact, solid state sensors suffer from long latency and long decay periods [3]. To overcome these problems, researchers use some artificial ventilation for their experimental setups. This strong and constant wind dominates gas spread creating a defined plume. Gradient-following techniques have been tested in this experimental environment [4], [5], and in [6] in an underwater environment with an artificial generated current. They show that the gradient-following based techniques work well only when the robot is inside the artificially generated plume. However, the information about the wind direction can be used as a mean to discover the source position. Some researchers also used the measured wind direction to trace an odor source [7]-[10]. It is well known that animal species use plumes of odor to locate preys, mates and other resources [11]. Therefore, for researchers, looking at the biological world as a source of inspiration for gas source tracing algorithms becomes natural. The most studied behavior is the upwind flight of male moths to track sources of female pheromone [12]. Male moths, in fact, can trace a pheromone emitting source even at very long distance in a turbulent environment. It is worth noting that when tracing a plume, bird, fish, and insect tracks all cross the plume in a zigzag shape [13] in repetitive and reiterate movements. These underlying similarities and the intrinsic efficiency of these behaviors suggest that they can also be implemented in autonomous robots. The behaviors of Bombyx mori, Dung beetle and
II. SOURCE CHARACTERIZATION AND SENSORS CALIBRATION
In order to characterize the gas dispersion, some preliminary experiments on a real case were carried out. The source used is a 8.5 cm diameter circular dish, containing ethanol alcohol because of its volatility at room temperature and not toxicity. TGS 800 commercial sensors from Figaro Engineering Inc. were used to detect the alcohol vapors. These are metal oxide semiconductor gas sensors and in presence of alcohol vapors, internal resistance changes, based on a logarithmic function. Sensitivity can change from sensor to sensor. Short term drift or response in presence of humidity can be also slightly different. For these reasons some calibration sessions were carried out. The sensors (6 sensors: 5 for source characterization, 1 mounted on the robot) were tested in an hermetically closed box, where a known increasing amount of alcohol was periodically injected. A fan was also included to accelerate alcohol vaporization and homogeneous dispersion. The signal coming from the sensors was acquired using a DAQCard-6024E™, from National Instruments. Data were sampled every 1 ms, and each stored value was the mean calculated on 500 measurements. In order to calibrate sensors, collected data were fitted with the characteristic bi-logarithmic function given by the sensors datasheet. Best fitting parameters were extrapolated (Fig.1). A third degree polynomial was used to fit the data. The found coefficients of the polynomial were used to convert sensor voltage output in concentrations during the experiments with the robot.
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Escherichia coli have been investigated in [4], [14]. However, they have been tested in an environment with the presence of artificial ventilation. In [15] an underwater robot has been used to track a source using a moth inspired algorithm and relied on information given by the measured flow. Finding an ejecting odor source is a not trivial task, particularly in absence of a forced airflow. In this case, in fact, plumes are not well defined and odor distribution is patchy and chaotic (Fig.3). Turbulence plays a major role. This paper addresses this issue describing SPIRAL (Searching Pollutant Iterative Rounding ALgorithm) algorithm, with its experimental results. SPIRAL can operate in an environment without a strong and constant airflow, because it does not rely on information about the wind to work. This algorithm was presented in [16] with some simulations aiming to explore its feasibility. It is inspired by the repetitive movements (casting) of insects searching for an odor source. SPIRAL algorithm has been implemented and tested on MOMO robotic platform. Moreover, the SPIRAL algorithm functionalities have been compared with two other algorithms: a bacterium inspired algorithm (BA) and a pseudo-random SPIRAL algorithm.
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Alcohol Concentration (mg/l)
Fig.1. Results of the calibration experiment. The bi-logarithmic graph shows the ratio “sensor resistance in the presence of alcohol” – “sensor resistance in clean air” vs “alcohol concentration” for a group of sensors.
III. PROBLEM STATEMENT 3.1 Localizing a source in a windless environment In an indoor environment with no strong airflow, the gas spreading mechanism is characterized by turbulent diffusion. In fact, while at low Reynolds number the chemical distribution evolution is dominated by molecular diffusion, at medium and high Reynolds number turbulence characterizes the evolution of gas dispersion [17]. The distribution becomes patchy and intermittent. The gas concentration is not homogeneous, but rather concentrated in “packets”. There is the presence of many local maxima and the absolute maximum is often located at a distance from the source if the source has been active for some time [18]. It is worth noting that concentration peaks, higher and steeper going towards the source, have been observed [19], [20]. During our source characterization we noted that those peaks are more frequent nearer to the source (Fig. 3). The use of artificial wind reduces these problems because the generated gas plume, as noticed in [5] and [6], consists of two zones: the region nearest to the source consists of high gas concentration odorant, which falls off sharply at the edges of the plume and with the average concentration decreasing going downwind. Going downstream the plume becomes larger and the edges are less defined. This is the far region where the instantaneous concentration is highly variable [6]. Figs. 2-3 show some graphs of the source characterization experiments with and without the presence of artificial ventilation. In Fig.2 a fan is placed at the back of the source. Wind velocity is approximately 0.5 m/s The windless environment is therefore a difficult environment for a robot tracing a source for two reasons: the first reason is that the concentration is patchier and a gradient is not well defined; the second one is that the robot does not have information about the direction of wind for upwind movements. However, an environment with no strong and constant ventilation can represent in a more accurate way a real indoor area. Works on gas source localization in an indoor environment, where no artificial ventilation is used, are
quite limited [20],[21],[22]. In an indoor environment it is very difficult to measure the present weak convective airflows, because of the state-of– the-art anemometers detection limits (approximately 5 cm/s) [22].
Source characterization with a strong airflow
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Fig.2. Source characterization with a strong airflow. Airflow is forced by a fan in the back of the source. Sensor0 is at a distance of 30 cm. All sensors are spaced 25 cm, so that the farthest sensor (Sensor4) is at a distance of 130 cm. Sensors are placed inside the plume. Source characterization without a strong airflow 2 1,8 Concentration [mg/l]
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Fig.3. Source characterization without a predominant airflow. Sensor0 is placed at a distance of 30 cm from the source. All sensors are spaced 25 cm, so that the farthest sensor (Sensor4) is at a distance of 130 cm.
IV. SPIRAL ALGORITHM 4.1 Algorithm Introduction The proximity to the source cannot be easily detected simply by instantaneous gas concentrations [20], and, moreover, the slow response and recovery time of sensors usually make gas instantaneous measurements more difficult. To solve these problems and to have an idea of the proximity to the source, SPIRAL uses a stop and sense philosophy, for sensing the gas, and it calculates an index, called Proximity Index, based on the average of the signal and on the measured concentration peaks. 4.2 Proximity Index The definition of the Proximity Index is the key point of SPIRAL algorithm. The PI parameter is essentially based on the intensities of measured concentration peaks and on the mean of gas concentration intensity. The PI is created
after an acquisition. The acquisition is taken when the robot is not moving, and lasts ΔT seconds. The PI is defined as: (1) PI = K μ ⋅ μ + K P ⋅ P(ΔTP )
Where: Kμ, K p are two multiplicative constant values; - μ is the mean of the signals acquired by the gas sensor during the fixed temporal window; - P is defined as a sum of the number of peaks of gas concentration, measured in a fixed temporal window (ΔT), multiplied by their own intensities. The acquisition temporal window (ΔT) is divided into intervals of ΔTp seconds; for each interval, only the highest peak is considered for the final sum; - it is considered “peak” a local maximum above the average value measured in the acquisition window; - ΔTp is the length of the windows into which the acquisition time is divided. The mean (μ) is added to the PI, to catch some information on the distance from the source, when the peaks are low. The values for these parameters are chosen on the base of the source characterization experiment data (in absence of wind). The PI calculation was applied to the complete source characterization data several times, by using each time a different set of parameters. The set was generated heuristically within reasonable limit values. The set of parameters that generates better PI was chosen for the implementation of the algorithm. The parameters are the following: ΔT (the fixed acquisition temporal window) is a trade-off between searching algorithm rapidity and PI creation accuracy and it is fixed to 30 s; ΔTp is fixed to 5 s (chosen among the following tested values: 1 s; 2 s; 5 s; 10 s; 15 s). ΔTp is an important parameter because it affects spurious peaks readings. Kμ is fixed to 1 and Kp is fixed to 0.5 (they affect the relative weighting of the average measure and peaks intensities). In the rare case that no peaks are found, Kμ=2 is used. This correction is needed to give more weight to the average value in this specific case. In fact, from our experimental characterisations, it seems that Kμ=2 is a suitable value to match PIs in samples with the same average values, either in presence and in absence of peeks. 4.3 SPIRAL description Drawing inspiration from the behavior of insects, the SPIRAL algorithm reiterates fixed searching spiral figures. The moth for example alternates fixed and repetitive movements to come into contact with odors (casting), and then it moves upwind (surge) [12]. SPIRAL is basically a sort of casting (spiral movements) that sometimes is restarted during the searching task. The decision of restarting the spiral movement is taken on the basis of the calculated PI values. It is clear that we cannot use a surge movement because we cannot measure a stable
and constant wind direction. The robot moves along a spiral. At the end of each spiral arm, it stops and performs an acquisition. With the data provided by the acquisition, the robot calculates the Proximity Index (PI), which takes into account the intensities of concentration peaks and the mean value of the signal (see 4.2 for PI description). The robot has a stored PI value called TPI (Threshold Proximity Index). If the next PI is equal to or higher than the TPI, the TPI is refreshed to the new PI value, and a new spiral is started (we call this a HIT). Otherwise, if the new PI is lower than TPI, the TPI is not refreshed, and the current spiral is continued (we call this a MISS). Fig. 4 shows a diagram of the SPIRAL basic idea.
a certain distance (this distance is a design parameter depending on the geometry of the environment). These movements are intended to explore the environment randomly, when considerable PIs are not produced. An obstacle collision handling behavior has also been studied and added to SPIRAL. In case of an obstacle collision, the robot performs a pre-defined sequence of movements, to avoid future obstacle collisions. The obstacle collision handling behavior is very simple: the robot goes backwards, then rotates and finally goes forward to cover the remaining distance it had to cover before the collision. The sense of the rotation (clockwise or anti-clockwise) is chosen taking into account either the task of continuing the spiral movement or the possibility of exploring places with higher PIs. Fig. 5 reports the flowchart of the SPIRAL algorithm.
Fig. 4. SPIRAL algorithm basic idea.
Moreover SPIRAL presents the following features: • A minimum threshold for PI: mTPI (Minimum Threshold PI). This threshold is a minimum PI value under which HIT mechanism does not trigger. The threshold was chosen on the base of experiment results. We calculated different PIs in different positions in the room used for experiments with the robots, after a source was ejecting ethanol for 5 minutes. Then, the PIs calculated at more than 2 meters from the source were considered produced by “noise”. The average of them was selected as the threshold. • A mechanism of TPI lowering. This mechanism is activated in two different cases: o five consecutive MISSes; in this case the TPI parameter is set to the value mTPI + Δ (where Δ is a constant design parameter); o three consecutive MISSes with measured PIs lower than TPI/2; also in this case, TPI is set to the value mTPI + Δ. The first mechanism aims to solve the problem of a robot moving in a gas low-concentration area, and the second one aims to prevent spurious high gas measurements (high PIs), which could compromise the SPIRAL efficiency. • An escaping (ESCAPE) movement. When a spiral has ended and no HIT has occurred during the spiral, the robot resets the TPI, and then it rotates of a randomly generated angle (45 degrees granularity). Afterwards, the robot goes ahead over
Fig.5. State diagram of the SPIRAL algorithm.
V. SPIRAL IMPLEMENTATION ON THE MOMO PLATFORM SPIRAL algorithm has been implemented on the MOMO robotic platform, supplied with a TGS 800 from Figaro Engineering Inc. sensor, for alcohol detection. The MOMO platform has been developed and presented in a previous work of the authors [16] to easily test gasfinding algorithms. MOMO is a multi robot platform, which consists of a variable number of small-size low cost single-agent robots (based on RoboDesigner Kit, from JAPAN ROBOTECH LTD), and of a PC. The central PC tracks and localizes the robots, supervises and collects data for the searching task, and dispatches intra-robot messages via wireless radio-frequency communication for cooperative multi-robot tasks. Fig. 6 shows one MOMO robot.
Fig.6. The MOMO platform overview with one robot.
move. Fig.9 shows a picture of the experimental area with a robot searching for the source. During the experiments, the robot speed is roughly 20 cm/s. During each acquisition, the robot samples the gas concentration each 1 ms and it stores averages calculated on 500 ms. A third degree polynomial was used to convert sensor voltage outputs in concentrations during the experiments, and its coefficients were calculated during the sensors calibration experiments (see II.).
One single autonomous robot was only used throughout the experiments for gas-searching algorithm validation. Fig. 7 shows the actual spiral movement the robot performs. The arm lengths have been chosen basing on the experiment searching area dimensions. The robot moves along this spiral path, by using odometry given by two encoders.
Fig. 9. Experimental area with the robot moving around, with the cardboard for webcam tracking.
VI. EXPERIMENTAL RESULTS Fig.7.Actual spiral path covered by the robot.
Fig.8 A drawing of the experimental room
Fig.8 shows a drawing of the room where the experiments have been performed. The robot is free to move in an experimental area of 3 m x 2.1 m, delimited by polystyrene walls on the north and south sides. The same room was used for calibration, preliminary experiments with the sensors and for the trials with the robots. The room was under controlled environmental conditions and humidity and temperature parameters were observed varying to a limited extent during all the experiments, influencing not significantly the measurements. During the experiments, the doors and windows of the room are closed. In the PC location, two persons are free to
Experimental results obtained from SPIRAL implementation on a MOMO platform robot are reported and discussed in this section. To evaluate the viability of the proposed SPIRAL algorithm a complete set of trials was performed implementing other algorithms for the robot movement. In particular, different sets of trials were carried out for each algorithm in two different starting conditions: 150 cm robot-source start distance, and 180 cm start distance. The trial is considered completed (stopping condition) when the robot reaches a 20 cm x 20 cm square, which contains the source. The trials were divided into sessions: four trials have been carried out for each session. Each trials session started five minutes after the opening of the gas source. At the end of each trials session, the air into the room is changed by opening the windows (to avoid too high concentration of alcohol inside the room). The experimental area is divided into nine sectors (see Fig.8). The source has been placed in sector 6. The robot starting position has been placed in sector 4: the robot is oriented with its nose directed 90 degrees with respect to the source. The robot positions are tracked by a webcam so that searching paths could be easily displayed and postprocessed. A typical tracked trial of SPIRAL algorithm is reported in Fig. 10.
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Fig.10. Webcam tracking of a trial.
The SPIRAL algorithm functionalities have been compared with two other algorithms: a bacterium inspired algorithm (BA) [4] and a pseudo-random SPIRAL algorithm. The bacterium inspired algorithm is the only algorithm in literature, to the best of authors knowledge, which uses only one gas sensor and does not require information about the wind to work. Description of the implemented E. coli algorithm is given in Table I. The acquisition time for the BA is fixed to 3 seconds [4],[14], which seems to be a suitable time to achieve reliable gas measurements. Moreover, we tried to investigate the behavior of BA with an acquisition time of 30 s, the same time used by SPIRAL. Table I. Pseudo code of E. coli bacterium algorithm. The E. coli algorithm: l=25 cm repeat { if current sensor reading is greater than previous sensor reading then rotate ± random(5°) and move forward l ± random(0.05l) else rotate±random(180°) and move forward l ± random(0.05l) }
The pseudo-random SPIRAL algorithm is implemented in the same way of SPIRAL, but using random values instead of Proximity Indexes. The SPIRAL random walk has been used to assess that the room dimensions are large enough to avoid that a random walk can find the source with a comparable number of acquisition with SPIRAL.
The results (means and standard deviations) of trials carried out using the three different algorithms are reported in Table II. Comparing the SPIRAL algorithm with pseudo-random SPIRAL seems to be evident the effectiveness of the method (in average SPIRAL reach the source in less than half the time spent by the random SPIRAL algorithm). Moreover SPIRAL works better than the bacterium inspired algorithm (both with 3 s and 30 s acquisition time), the only algorithm, to the best of authors knowledge, which works in absence of wind and uses only one sensor. The results of our trials with the BA (30 s acquisition time) suggest that, even if it finds the source with less acquisitions than BA (3 s acquisition time), it is much slower from a temporal point of view. This can be due to the nature of bacterium inspired algorithm. This algorithm has to perform a considerable number of acquisitions in order to orientate the robot using its random nature. The problems seem to appear when the robot moves into areas with low gas concentration. The long acquisition time makes the task of moving towards area with higher concentrations very time consuming. It is worth noting that the obstacle handling behavior becomes fundamental above all for BA. In fact, BA (3 s acquisition time version) suffers more than SPIRAL does from the turbulent effects and it is prone to wander hitting the borders of the experimental area. Further experiments using larger areas will be done in order to establish how the two algorithms behave. SPIRAL pays its larger robustness against turbulent effects with a quite long acquisition window. In the future we will try to optimize the parameters (like the length of the temporal window) to reach the best compromise between stability of convergence and time to find the source. Table II. Results of the trials with different algorithms. Algorithm
Starting distance from the source [cm]
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15
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E. COLI (acquisition time =3 s)
E.COLI (acquisition time =30 s)
SPIRAL RANDOM
μ = 398.2 σ= 151.5 μ = 306.3 σ= 91.2
Steps(Acqui sitions) to find the source μ = 11.5 σ = 4.3 μ = 9.2 σ= 2.7
15
μ = 463.3 σ= 93
μ = 67.8 σ= 14.7
μ = 150
15
μ = 353.1 σ= 73.2
μ = 50.3 σ=11.3
μ = 180
4
μ = 1671,2 σ=134.9
μ = 49.3 σ=4.1
μ = 150
4
μ = 180
10
μ = 1184.1 σ=158.1 μ = 1351 σ= 291
μ = 35.7 σ=5.1 μ = 38.6 σ= 8.1
μ = 150
10
μ = 991 σ= 193
μ = 28.3 σ= 5.2
Time to find the source [s]
VII. CONCLUSIONS AND FUTURE WORKS This paper focuses on the design and implementation of a biologically-inspired algorithm on a robotic platform, with the purpose to localize a gas source in an indoor environment in absence of strong airflows. Under these conditions, gas dispersion in the air becomes more turbulent, because turbulence and convection flows strongly disturb the diffusion process. Gas is present in “packets”. Therefore, searching an emitting source becomes a more complex task than in the presence of a strong wind. We propose the SPIRAL algorithm, which essentially consists of spiral movements and stop and acquisition steps, after which a Proximity Index is created. The Proximity Index aims to express the proximity to the source with a numerical value. The index includes the measured gas peaks intensity and the signal average as well. SPIRAL allows the robot to move towards higher and higher Proximity Index locations. SPIRAL algorithm has been implemented on the MOMO robotic platform. The algorithm was tested by means of experiments with a robot. The validity of the SPIRAL approach has been assessed versus traditional E. coli bacterium algorithm and SPIRAL random-walk in some experiments. The results are very encouraging and show that the algorithm is robust in the source-finding task. The main features of SPIRAL algorithm can be summarized as follows: • The robot spirally moves towards high Proximity Index locations; • The spiral movements make the algorithm intrinsically robust, which means that wrong measurements do not compromise its effectiveness; • Only one gas sensor is necessary. There is no necessity to calibrate more than one sensor. In fact it is not a trivial task to calibrate different gas sensors and above all to trust on relative measurements when the difference is not so high; • It works well without a constant forced airflow. Even if the time to find the source is a bit higher than algorithms in literature (the most of them uses an artificial wind in their experimental setups), we are working on optimizing the SPIRAL parameters using some PC simulations. To sum up, SPIRAL can be seen as a viable algorithm to use in an indoor environment when the airflow is so weak or unsettled that a state-of-the-art anemometer cannot measure it in a reliable way. In future works, further experiments aiming to optimize SPIRAL parameters will be described and discussed as well as experiments in larger area in order to investigate the SPIRAL efficiency.
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