A Biologically-Inspired Algorithm Implemented on a new Highly ...

A Biologically-Inspired Algorithm Implemented on a new Highly Flexible Multi-Agent Platform for Gas Source Localization 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 presents the design of a biologically-inspired algorithm, as well as the design and development of a new highly flexible multi-agent platform for a cooperative robotic system, to be applied to the localization of a gas source in an indoor environment with no strong airflow. The platform consists of a central PC and a variable number of robots. The robots cooperate, can communicate with each other, even when exchanging complex messages, and present a swarm-like behavior, which optimizes the gas localization task. The inexpensive, multipurpose, scalable, highly flexible platform whose use is discussed in this paper investigates the efficiency of bio-inspired cooperative algorithms, to detect the odor source location.

Index Terms – multi-robot system, odor source finding, cooperative swarm behaviour, and intra-robots communication I. INTRODUCTION This paper focuses on the design and implementation of a biologically-inspired algorithm on a robotic platform, which can be used throughout the gas source localization task. Gas source localization involves the challenge to rapidly localize an emitter source ejecting a chemical agent. The purpose behind it is to minimize the exposure of human agents to dangerous pollutants, and to use autonomous robots to localize the gas source. This fast localization system could have many applications in real environments. It could be used both to prevent human beings from intoxication and for environmental monitoring: for the detection of pipeline leakage in a domestic or industrial environment, for the prevention of carbon monoxide poisoning caused by fire or inadequate ventilation of obstructed stoves, and for the monitoring of a poisoned area after a chemical accident, to find its dangerous emitting source, just to give a few examples. Moreover, this fast localization system could be used to localize hidden explosives or narcotics and for automatic humanitarian demining. Finally, gas source localization .

may be useful in providing rescue after natural disasters, as it helps to find survivors, seen as carbon dioxide sources. The gas source localization task can be divided into three different subtasks [1]: plume finding – detecting an increased concentration, plume traversal – following the gas plume to its origin, and source declaration – confirming that the source has been found. Odor source localization with no strong airflow shows some additional difficulties: with no wind, the diffusion transport mechanism is overwhelmed by convection flow and turbulence. As a consequence, the concentration field is patchy and chaotic. With this in mind, [2] suggested a different taxonomy for gas source localization: gas finding – detecting an increased concentration, source tracing – following the cues from the sensed gas distribution, and source declaration – as a confirmation that the source has been found. Moreover, the pollutant mapping task – the mapping of pollutant concentration in a specific area - can be seen as complementary to gas source localization for monitoring tasks. In this respect, literature shows that many researchers have found animal approach particularly helpful in approaching the challenging nature of the source localization task. A wide range of species use plumes of odor to locate prey, mates, and other resources [3], but the most studied behavior is the upwind flight of male moths to find female pheromone [4]. It is worth noting that when tracing a plume, bird, fish, and insect tracks all cross the plume in a zigzag shape, to neutralize any turbulent effects [5]. These similarities and the intrinsic efficiency of these behaviors suggest that they can also be implemented in autonomous robots. [6] investigates bacteria behavior using a chemo taxis method; [7] discusses the implementation of a modified Bombyx mori behavior, adapted to an indoor environment with no strong airflow. In [8], Russel and coworkers study the anemotaxis strategies of Bombyx mori and Dung beetle, and Escherichia coli pure chemo taxis behavior. Finally, in [5], male moth behavior is studied. Many non bio-inspired approaches have also been chosen. We can cite Ishida and coworkers [9] for studies on odor compass, which provide meaningful measurements of gas concentrations, and for studies on algorithms [10] that use wind direction.

In [11], Lilienthal et al. investigate the case of an indoor environment with no strong airflow. To minimize source detection time and to improve search algorithms, multi robot algorithms have been studied. The purpose of using a sort of swarm intelligence as a method for solving distributed problems draws inspiration from the world of social insects, which emphasizes the parallelism of task solving and simplifies the single agent [12]. The use of a spiral surge algorithm with a multi-robot platform is investigated in [1]. In [13], Zarzhitsky et al. use a cooperative swarm of robots that move in a close formation, also using fluid-dynamics information. All these approaches show the necessity of a highly flexible, inexpensive robotic platform, to test multi-robot searching algorithms for gas source finding, declaration, and pollutant mapping. This paper describes a robotic platform, called MOMO (Multi-robots for Odor MOnitoring) platform. MOMO consists of a variable number of small-size single-agent robots and a PC. The central PC tracks and localizes the robots, supervises and collects data for the searching task, and dispatches intrarobot messages via wireless radio-frequency communication. The communication mechanism allows the use of meaningful structured messages among the robots, and therefore provides the opportunity to investigate cooperative multi-robot algorithms, based on complex data exchange. This platform is inexpensive, because commercial common electronic and mechanical components are used, and is flexible, as it is possible to modify the number of single agents with no heavy impact on the system. The robustness of MOMO platform is guaranteed both by the single robot distributed intelligence, and by the central PC supervising activity. Finally, the platform is multipurpose, because it can be easily used - with no hardware changes - throughout the different subtasks composing the gas source localization macrotask. The main goal of this paper is to describe the features and efficiency of MOMO robotic platform. In future work, MOMO will be used to compare classical existent searching algorithms with new research strategies, and to make best use of the platform great communication skills. II MATERIALS AND METHODS 2.1 MOMO overview The main purpose behind MOMO robotic system consists in designing and implementing a low-cost flexible system, to test gas searching algorithms in an indoor environment. MOMO essentially includes two different sub-systems: a PC and a variable number of moving robots. The two subsystems strongly interact with each other: the PC tracks the robots position by means of a web-cam, collects data from each robot, and monitors the state of the searching agents, by exchanging messages with them via a RF communication system. The single robots perform the actual gas searching task. They can move independently avoiding obstacles (either the searching area limits or other moving robots), and they can communicate with each other

via a RF channel, using the central PC as a dispatcher server. The idea of combining central (the PC) and distributed intelligence (robots) aims to provide high flexibility, based on a variety of possible implemented algorithms. MOMO offers the possibility to easily test either algorithms that use distributed intelligence or algorithms that also use central intelligence. In fact, using the PC as a sort of global coordinator offers the possibility to have the robots totally move autonomously, by exchanging their local knowledge with one another, or to use the knowledge acquired by the robots, towards the acquisition of a more accurate understanding of the environment and consequently of the source location. 2.2 Tracking systems and communication For the tracking system, a commercial web-cam has been secured to the ceiling of the room where the experiments are performed. Each robot has a white cardboard at its top, with two different color circles. From the two circles, the PC recognizes the position and the orientation of the robots, based on a reference frame integral with the room. The area for the experiments is 4 x 3 m, surrounded by white 15 cm-high polystyrene walls, to be detected by the robot proximity sensors. The radio communication transceiver used is a AUR EL XTR-434H [14] component, connected via a USB-RS232 adapter to one of the PC USB ports. The XTR-434H is a commercial FM transceiver, working at a frequency of 434.42 MHz. The serial communication speed between the PC and the transceiver is 57600 Baud. 2.3 Robot architecture Each robot constituting a single agent has been designed based on the RoboDesigner Kit, from JAPAN ROBOTECH LTD [15]. The robot includes the following subsystems: • Motor Control Micro and DC Motors: the micro controller comes from the RoboDesigner Kit. It is connected to the Main Micro Controller through 4 digital channels. From these channels, it continuously polls the motor commands (direction and speed), and through PWM, it pilots the two DC motors coherently. • Power supply: high-capacity rechargeable Ni-MH batteries. • Main Micro Controller – it is the main robot controller. It is a PIC 18F452 micro controller from Microchip Technology Inc [16]. It reads all the sensors, it processes the data, and decides the movement (by driving the Motor Micro Controller). • Gas Sensors – the gas sensors currently mounted on the agents are two commercial TGS 800, from Figaro Engineering Inc. These sensors detect the presence of ethyl alcohol. Ethyl alcohol has been chosen to test the platform, because it is simple to handle and harmless for human health. • Infrared Leds and IR Detectors – for these subsystems infrared leds and IR detectors from RoboDesigner Kit are used. These items act as proximity sensors for the

robot, and are used to implement an obstacle avoidance behavior (obstacle avoidance refers either to the avoidance of intra-robot collisions or to collision between the experiment area limits and the robots). • Encoder – one encoder is mounted on a wheel, to give the robot some rough odometry. • RF Transceiver – the same transceiver is also used with the PC. For a more detailed description see Section 2.2. Fig. 1 shows a picture of a robot prototype.

2.5 Gas source characterization The emitting source used throughout the experiments with the robot is represented by a small dish containing alcohol free to evaporate. The source is located in a closed room, in the presence of people, with no predominant air flow. In these conditions, a turbulent dispersion of gas occurs. The TGS 800 sensors were used for a preliminary characterization of the alcohol emitting source, to experimentally find some significant parameters for the turbulent gas dispersion. Fig. 3 shows the position of the six sensors (Ch0-Ch5), placed near the source. The signal from the sensors was acquired for 15 minutes using the DAQCard-6024ETM.

Fig.3 Positioning of the six sensors (Ch0-Ch5) and of the alcohol source, throughout the source characterization experiment. Fig. 1 Picture of a Robot. The robot size is 17 x 17 cm.

2.4 Gas sensors calibration The alcohol sensors (TGS 800 from Figaro) mounted on the robot are bulk metal oxide semiconductor sensors. In the presence of alcohol vapors, the sensor resistance changes, based on a logarithmic function. However, the sensor sensitivity can strongly change from sensor to sensor. With this in mind, a calibration session was carried out. All the sensors used (6 sensors) were tested in a hermetically closed chamber, where an increasing amount of alcohol was injected. In the chamber, also a fan was included to accelerate the vaporization of the injected alcohol and its dispersion. The signal coming from the sensor was acquired using a DAQCard-6024ETM, from National Instruments. Fig. 2 shows the results of the calibration test. The calibration parameters extracted from the calibration experiment were used to correct the sensors.

Fig. 4 reports the results of an acquisition, after being corrected based on the calibration parameters obtained from the calibration experiments. As expected, gas behavior is quite turbulent; gas is detected as spots or packets, providing characteristic peaks, frequently and intensely closer to the gas source. The empirical parameters extracted from the experiments (peak intensity, frequency, and velocity) were used to implement the distribution of gas concentrations for the simulation.

Fig. 4 Results of the source characterization experiments.

2.6 Gas dispersion model for simulation

Fig. 2 Results of the calibration experiment. The graph reports the ratio “sensor resistance in the presence of alcohol” – “sensor resistance in clean air”, versus alcohol concentration for each sensor.

The description of gas dispersion in an environment, particularly in the absence of a strong airflow, is very complex, owing to turbulence chaotic effects. Consequently, to model this phenomenon with a convincing mathematic model is an extremely difficult task. In literature, simple models that use wind are available [17]; these models derive from the solution of the

equation on turbulent diffusion [18]. However, the proposed concentration is a time-averaged one. The idea, also suggested by our first experiments with Figaro sensors, consists in using a simple model for our simulations, which aims to catch the spiky and patchy gas distribution as instantaneously measured by sensors. This gas dispersion model will be used in our software simulations. We model gas distribution as peaks of concentrations with Gaussian distribution. The generic point of the coordinates X i and Yi will present an intensity J X i ,Yi given by (1).

J X i Yi = ∑ k

Jk e σ k 2π

− (( X i − X k ) 2 + (Yi −Yk ) 2 )

where J k is the intensity,

2σ k 2

σ

(1),

2 k

is the variance, and X k

and Yk are the coordinates of the center related to k-th peak . The peaks are spread in a random way around the emitting source. As the distance from the source increases, the peak parameters change: their spatial frequency and intensity decrease, whereas their variance grows. This distribution gives sharper peaks showing higher intensity near the source, and smoother peaks when going far, to simulate the turbulent diffusion phenomenon with no preferential wind direction available. From a temporal point of view, the distribution evolves with a linear interpolation between different randomly generated distributions. In so doing, it preserves the overall mass of the system; which is identified as the integral of intensity in the simulation area. Fig. 5 shows an image of a randomly generated distribution.

airflow. With this idea in mind, we suggest a cooperative algorithm, biologically inspired to Bombyx mori behavior, aiming to detect frequency increments in concentration peaks. The idea consists in joining the fixed and repetitive pattern of insect movements with the use of a swarm of robots, to optimize the searching task. Obviously, the absence of a predominant airflow in the room does not allow to exploit the upwind surge movement, so peculiar to insect behavior. Fig. 6 shows an outline of the process. The proposed algorithm can be divided into two phases: • Individual search for locations with high frequency of gas peaks at high concentrations. A sort of independent robot movement along the gradient of gas peak frequency; • Cooperative search involving the whole swarm of robots. In the first phase, each robot moves along a spiral pattern monitoring gas concentrations through the two TGS 800 sensors. The algorithm uses the mean value of the two measurements, to improve the quality of the monitoring task. As inputs, the algorithm needs the following parameters: a so-called source-threshold Sth and initial robot thresholds, Thi. If during the spiral movement, a robot finds a peak frequency higher than Thi, it restarts a spiral movement and it updates Thi with the new value found. If this does not happen within a given time, the robot restarts a spiral movement. The first phase comes to an end when a robot, for example robot j, discovers a peak frequency higher than Sth, during its spiral movement. First of all, robot j refreshes Thj with the new discovered peak frequency, then it starts another spiral and “calls” for the other robots: while continuing to measure gas concentrations, they start approaching robot j. If one of them discovers a peak frequency higher than Thj, robot j refreshes its own Thi and it starts another spiral, “calling” for the others to reach it, to localize the source with the whole swarm movement.

Fig. 5 Distribution of the simulated gas concentration

2.7 Bio-inspired cooperative algorithm This paper discusses an algorithm that aims to find high frequencies of gas peaks in indoor environments. In fact, as shown in [11], and as demonstrated by our preliminary sensors calibration, high gas peak frequency can reveal the presence of a gas source in an environment with no strong

Fig. 6 Diagram of the cooperative bio-inspired algorithm

In case it reaches its destination, robot j starts a new spiral. The process goes on until the source is discovered (to be declared with other methods), or until a predefined time elapses. Based on the algorithm idea, the cooperative stage can improve the task of localizing a gas source. This algorithm was tested with the help of simulations, and it will be then implemented on the MOMO platform. Fig. 7 shows an overview of the MOMO platform.

behaviors and algorithms are implemented through a highly flexible communication system. Every single agent can either communicate with the central PC or with other robots. This architecture allows the platform to perform different typologies of experiment and validate algorithms with different degrees of cooperation and swarm behavior. A preliminary validation of the communication system was performed, exchanging messages that contain commands for movements and gas concentration data. 3.1 Simulation results

Fig. 7 An overview of the MOMO platform

III RESULTS AND DISCUSSION Each agent belonging to the MOMO platform was designed using the RoboDesigner Kit. Thus, small and inexpensive robots were built. The implemented structure is very compact and fulfils high handling skills. Power supply and DC motors could be scaled even more if necessary. Infrared Leds and IR Detectors, added on each robot, allow an optimal obstacle and intra-robot collision avoidance behavior. The functionalities of the robotic system were tested and validated. The robots can move autonomously, according to different algorithms implemented in the built-in controller. In addition, the robots can be controlled remotely by a software running on the central PC. Based on the images acquired by the webcam, the software can drive any agent with high accuracy from its current location to a desired destination, and gives the whole structure an overall robustness, efficiency, and flexibility. The web-cam based control is able to detect (in real time) the position and the orientation of each robot with a suitable resolution (less than 1 cm for the position and 3 degrees for the orientation angle), and good accuracy. The main microcontroller can acquire the signals, provided by the alcohol sensors (TGS 800) mounted on the robots, with high sample frequency (more than 1kHz) and a resolution of 10bits. The relative resistance measured (R/R0: resistance in the presence of alcohol/resistance in clean air) is related to alcohol concentration through an almost bi-logarithmic function (see Fig. 3). A polynomial function was used to perform the fitting of data obtained from the calibration experiments, expressed as alcohol concentration logarithm vs. relative resistance (R/R0). Using the parameters extracted from the fitting, the real gas concentrations could be calculated with logarithmic accuracy over the dynamic range (20 mg/m3 - 20000 mg/m3). The cooperative

The simulation tests aimed to investigate the theoretical efficiency of the developed algorithm in a turbulent environment. The simulations were performed using a mathematical software (Matlab). The robots moved on a grid of 200 by 200 points, with a speed of one point per turn. The gas dispersion model used to describe a turbulent environment has been previously described (see Section 2.8). The algorithm performances have been assessed against a pure-random walk. The first tests were performed using a single agent, with the purpose to validate the basic viability of the algorithm. Afterwards, we validated the efficiency of the proposed multi agents approach in comparison with the use of a single agent. The simulations ended when one robot reached a circle with a radius of 5 units. Two parameters were used to assess the efficiency of the algorithm: the steps necessary to reach the source (a 5 unit radius), and the average distance of the robot from the source. Tables 1 and 2 show the results of the simulation with one single robot. Table 1 Simulation results with one single robot, with source in (100,100) Algorithm Source Robot Steps to Average location starting find the distance point source from source Random walk (100,100) (180,180) μ=7173 μ=84 – 1 robot σ2=3535 σ2=8 Spiral – 1 (100,100) (180,180) μ=1611 μ=64 robot σ2=155 σ2=6

If compared with the proposed algorithm, the random walk behavior certainly needs more steps to reach the source, and presents a longer average distance from the source. The robot moving according to our algorithm first phase clearly converges to the source location. Table 2 Simulation results with one single robot, with source in (40,40) Algorithm Source Robot Steps to Average location starting find the distance point source from source Random walk -1 (40,40) (180,180) μ=7169 μ=125 robot σ2=3414 σ2=8 Spiral – 1 robot (40,40) (180,180) μ=2583 μ=112 σ2=304 σ2=13

Table 2 shows the results of simulations with longer distance between source location and robot start position. One can easily note that whereas the random walk behavior presents the same statistical parameters as the previous table, the robot moving with our algorithm

presents an increased number of steps to detect the source. Obviously, this depends on the longer distance, but the robot converges to the source in any case. Table 3 shows the results of simulations with three robots. One can see that, on average, the proposed cooperative algorithm detects the source before the random walk. Moreover, it shows a lower variance; therefore, it gives stability to the convergence to the source. Table 3 also reports results for a single robot starting from (180,180). Clearly, results get worse than for the cooperative algorithm involving three robots. Fig.8 shows the trajectories of the three robots during one of these simulations.

validation of the gas source algorithm in experimental conditions. The platform consists of a variable number of small size single-agent robots, a PC, and a web-cam. The robots can cooperate and interact with each other through a radio-frequency communication channel and exchange data (i.e. gas concentration or position data) at the same time. MOMO platform is inexpensive and scalable. A preliminary test of the platform functionality with three robots has also been successfully carried-out. Next step will involve the implementation of the developed multiagent algorithm on the MOMO robotic platform, for validation in a real indoor environment.

Table 3 Simulations results with 3 robots (random and spirally) and one robot spirally Algorithm Source Robot Steps to Average location starting find the distance point source from source Random walk-3 (180,180) μ=108 μ=3745 robots (60,60) (60,180) σ2=16 σ2=2794 (180,20) Spiral -3 robots (180,180) μ=98 μ=1353 (60,60) (60,180) σ2=21 σ2=171 (180,20) Spiral -1 robot (60,60) (180,180) μ=2187 μ=104 σ2=377 σ2=3

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Fig. 8 Trajectories of the three robots and gas distribution

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IV. CONCLUSIONS AND FUTURE WORKS

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This paper focuses on the design and implementation of a biologically-inspired algorithm on a robotic platform, which can be used throughout the gas source localization task in an indoor environment with no strong airflow. In the absence of a strong airflow in an indoor environment, the dispersion of a gas in the air may involve a turbulent process, and the concentration of gas may not be homogeneous, but rather concentrated in “packets”. Therefore, the search for the emitting source becomes quite a complex issue. With this goal in mind, a new bioinspired algorithm based on multi agent cooperation was developed and extensively tested. A series of experiments were carried out to characterize the gas source with real sensors, to provide a set of parameters for the simulation. The simulation results are very encouraging and show that the algorithm works well either with or without singleagent robot cooperation. A platform called MOMO (Multirobots for Odor Monitoring) was designed for the

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