Robotic Sensor Networks: An Application to Monitoring Electro-Magnetic Fields 1

Robotic Sensor Networks: An Application to Monitoring Electro-Magnetic Fields 1 Francesco AMIGONI a,2, Giulio FONTANA a and Stefano MAZZUCA a a Dipartimento di Elettronica e Informazione, Politecnico di Milano, Italy Abstract. Robotic sensor networks are distributed systems in which mobile robots carry sensors around an environment to detect phenomena and produce detailed environmental assessments. In this paper we present a specific robotic sensor network devoted to monitoring electro-magnetic fields. Keywords. Robotic sensor networks, Multirobot systems, Electro-magnetic fields. Introduction Many applications require to monitor an environment in order to detect phenomena and produce detailed environmental assessments [14,22]. These applications, including surveillance, plant and environmental monitoring, and damage assessment, are usually addressed using sensor networks, in which sensing nodes are set in fixed locations [26]. The use of multirobot systems for carrying sensors around the environment represents a solution that has recently received considerable attention [1,21] and that can provide some remarkable advantages. For example, these robotic sensor networks can dynamically concentrate the sensing robots around a place of interest. In this paper we present a specific robotic sensor network oriented to monitoring Electro-Magnetic Fields (EMFs). The monitoring of EMF phenomena is extremely important in practice, especially to guarantee the safety of people living and working where these phenomena are significant. It is thus important to localize and monitor the EMF sources in an environment and to assure that the EMF levels are compatible with human exposure [8]. These activities can be naturally carried out by robotic sensor networks, such as the one described in this paper. A robotic sensor network does not require any fixed infrastructure and thus its use is appropriate when a single campaign of EMF measurements is required (e.g., before the opening of a new hospital). The robotic sensor network presented in this paper has been developed as part of a larger project, illustrated in [1]. The architecture of our system is hierarchical. A coordinator (a computer) supervises the activities of the system, while a number of explorers (mobile robots equipped 1 Preliminary versions of this work appeared in [3,5]. 2 Corresponding author: Francesco Amigoni, Dipartimento di Elettronica e Informazione, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy; E-mail:amigoni@elet.polimi.it.

with EMF sensors) navigate in the environment and perform EMF measurement tasks. In our implemented system there are two explorers. The main cycle of activities of the robotic sensor network we implemented can be summarized as follows: (a) the explorers (independently) measure the EMF in their current positions, (b) the coordinator integrates these measurements and builds an hypothesis about the location of the EMF sources, (c) the explorers move in the environment to reach the new interesting measurement positions determined by the coordinator; then the cycle starts again from (a). We explicitly note that the explorers are autonomous mobile robots that carry both navigation sensors (for example, cameras and sonars) and monitoring sensors (such as the EMF sensors). This paper is structured as follows. The next section reviews the relevant literature on robotic sensor networks. Section 2 describes the robotic sensor network we developed to monitor EMF sources. Section 3 presents the experimental validation of our system. Finally, Section 4 concludes the paper. 1. Robotic Sensor Networks A sensor network (SN) is a collection of sensory elements or nodes that gather, process, and communicate data about environmental phenomena [26]. Practical applications of SNs are countless: the need for sophisticated monitoring systems is growing in several fields, including scientific research, traffic control, medical applications, environmental protection, disaster management, industrial plants, and military applications. A rough classification distinguishes between fixed sensor networks, in which nodes are spatially distributed in fixed locations, and mobile sensor networks, in which some or all of the nodes can move around the environment [15]. The interest about mobile SNs is motivated by the need to have SNs that are not part of the system to be monitored (e.g., an industrial area), but that can be deployed on location in a short time to cope with fastevolving and partially-unknown phenomena. To monitor such phenomena with a fixed SN, a large number of nodes scattered over a broad region is required. This implies using very simple, low-cost, low-power, disposable sensory elements with limited processing and communication capabilities [12]. An alternative could be the use of a mobile SN composed of a smaller number of nodes, initially deployed randomly within the area, but able to reach interesting locations to optimize observations and communications. Mobile SNs typically rely on wireless communication systems [22], because wired links are incompatible with their design goals, in particular with a fast deployment phase. Mobile nodes can be implemented as autonomous perceptive mobile robots [21] or as perceptive robots [2], whose sensor systems address both navigation and environmental monitoring tasks. In this sense, robotic sensor networks are particular mobile sensor networks. The sensing robots can be more complex and expensive than sensing nodes typically used in a fixed SN, enabling them to perform complex processing and communication tasks. Such tasks can include the efficient and autonomous navigation through the environment, the prediction of the evolution of the observed phenomena, and the planning of the actions to be undertaken in order to optimally track the phenomena and to perform sophisticated monitoring, reconfiguration, and self-repairing actions. The main

drawback of the multirobot approach to SNs is represented by the intrinsic complexity and the high cost of the robot platforms. A number of applications have been addressed by robotic sensor networks, including environmental monitoring [23] and search and rescue [24]. In the sequel of this section, we review some robotic sensor networks that have been presented in literature. Some of the proposed systems are only simulated (see, for example, [11,17]) or are composed of a single robot (see, for example, [18]). Some works addressed the definition of an architecture for a robotic sensor network. For example, the hierarchical architecture presented in this paper (and similar to that discussed in [4,6]) differs from the architecture proposed in [21] since, in our case, the coordinator is responsible for data integration and for building the model that represents the perceived phenomenon (essentially, the locations of EMF sources), while, in [21], all the sensing robots are involved in the integration of the perceived data. Although hierarchical architectures have some known drawbacks (e.g., scalability), they are easy to implement and adequate for a small number of sensing robots, as in our case (see [19] for another example related to odor maps). Another architecture for robotic sensor networks, based on the decentralized data fusion approach, is proposed in [13]. Many works in literature address a single aspect of robot sensor networks: the deployment of sensing robots [15,16,27,28]. Usually these approaches use a local distributed strategy to drive the spatial configuration of the robots: each robot adjusts its position according to those of its neighbors, for example using optimization algorithms based on potential fields. Our architecture adopts a global centralized strategy to deploy the sensing robots in the environment: the explorers move in the positions that the coordinator globally evaluates as interesting. In this sense, our approach shares some similarities with that in [7]. The advantage of our approach is that, usually, local approaches are able to deploy sensing robots only to cover an environment [10,20]. There have been some attempts to define local distributed strategies to let sensing robots converge toward events of interest. For example, [9] presents some control strategies similar to potential fields that allow robots to concentrate around events happening in the environment; however, the methods are tested in simulated environments without any reference to a specific application. 2. A Robotic Sensor Network for Monitoring EMFs In this section, we illustrate a specific robotic sensor network we have developed in our Artificial Intelligence and Robotics Lab (AIRLab). The system we present here is oriented to localize and characterize a (possibly moving) EMF source. We first describe the general architecture of the system, then the localization algorithm it uses. 2.1. General Architecture As said, the architecture of our system is hierarchical with a coordinator supervising two explorers (more explorers can be easily added). We assume that the three agents can communicate with each other by exchanging messages. The system maintains a grid map of the environment, in which each cell can be either free or occupied by an obstacle (or by a robot). The map is supposed to be known by the coordinator and the explorers. The

RECEIVE FROM ROBOTS THEIR INITIAL POSITIONS SEND INITIAL POSITION TO COORDINATOR RECEIVE FROM ROBOTS THEIR EMF MEASUREMENTS PERFORM EMF MEASUREMENT PERFORM FINE-GRAINED IF SOURCE EMF MEASUREMENTS CAN BE LOCALIZED TRUE AROUND THE SOURCE FALSE GENERATE A NEW PROPOSED TASK SEND PERCEIVED DATA TO THE COORDINATOR RECEIVE A PROPOSED TASK FROM COORDINATOR SEND THE PROPOSED TASK TO ROBOTS BUILD A PATH TO FULFILL THE PROPOSED TASK RECEIVE PROPOSED PATHS FROM ROBOTS SEND THE PROPOSED PATH TO COORDINATOR CONSIDER THE MINIMUM LENGTH PROPOSED PATH P min REFUSE P min AND ELIMINATE IT FROM PROPOSED PATHS FALSE IF THE PATH IS ACCEPTED TRUE IF P min CONFLICTS WITH ALREADY ASSIGNED PATHS FALSE ASSIGN THE TASK TO THE ROBOT AND CONSIDER IT AS BUSY RECEIVE FROM BUSY ROBOTS THEIR CURRENT POSITIONS TRUE EXECUTE THE ACCEPTED PATH DETERMINE THE NEW CURRENT POSITION SEND CURRENT POSITION TO COORDINATOR Figure 1. The flows of activities of coordinator (left) and of explorers (right) environment is assumed to be static (i.e., the positions of the obstacles do not change while the system is working). The map is used by the explorers to navigate in the environment and by the coordinator to localize the EMF sources. For simplicity, we consider a single EMF source. The position of the EMF source is thus given by the cell of the map in which the source is located. The activities of the coordinator and of the explorers during the EMF monitoring of an environment are schematically reported in Figure 1. EMF monitoring involves two activities: localizing the source of the field and characterizing the source, namely checking if the emissions of the source are compatible with human exposure. The localization of the source is performed according to the algorithm described in the next section. Once a source has been localized, it has to be reached by an explorer both for confirming the position and for more detailed EMF measurements in the region around the source (characterization). In this paper, we focus on the localization of the source. We describe one of the most significant features of our system: the task allocation. We use the contract net paradigm for allocating tasks to the robots [25]. The allocated tasks are EMF measurements to be performed in given positions. The coordinator proposes a cell for visit if it needs an EMF measurement from that cell, either for estimating the position of the source (see next section) or for determining whether the EMF is within the limits established by the law for human exposure. The coordinator sends the proposed task to free explorers, waits for their proposed paths (or until a timeout expires), and assigns the task to an explorer. A proposed path is a sequence of movements that brings an explorer from its current position to the cell to be visited. The coordinator considers the proposed path of minimum length and assigns the task to the proposing explorer. The other explorers are notified that their proposed paths have been rejected. For more details on the architecture, please refer to [5].

2.2. The Localization Algorithm In this section, we discuss the algorithm for the localization of the EMF sources that represents the core algorithm of our robotic sensor network. In general, a localization algorithm strongly depends from the available sensors. The sensors we used have been developed by the Dipartimento di Elettrotecnica of the Politecnico di Milano and are extensively described in [8]. They can detect only the magnetic field, but they still allow to localize a EMF source. Each sensor is connected through an RS232 serial link to the computer that governs the explorer. We used two sensors, mounted on the two explorers at (approximately) the same height. For each spatial component of the magnetic field, the sensor outputs the modulus of its DFT (Discrete Fourier Transform) evaluated at 10000 frequency points separated by nearly 1 Hz intervals in the range 10 Hz 10 khz. Note that, since the sensors we use are isotropic, the coordinator requests a measurement from a position, without specifying the heading. Note also that, when the coordinator requests a measurement from a position, the measured data will be the same if this measurement is performed by one explorer or by the other one. Our localization algorithm is based on the following general assumptions: The environment contains a single, possibly moving, source of magnetic field. Other less powerful sources can be present, but we require the magnetic field generated by the main source to be (as measured by the sensor) significantly stronger than the background field in the environment. In each of the measurement positions the magnetic field vector has a non-null radial component and a null tangential component, as evaluated in the twodimensional polar coordinate system defined in the horizontal plane H containing the sensor and centered on the projection of the source on H. In our experimental setting, we enforced this by using a solenoid with its axis orthogonal to the floor as source, by performing near-field measurements, and by avoiding to take measurements on the horizontal plane containing the source. No a priori assumption is made on the spectrum of the magnetic field produced by the source. However, since the sensor we employed ignores the frequency components outside the 10 Hz 10 khz range, it can only detect sources which have significant magnetic emissions in that frequency band. The localization is based on triangulation, which in turn is based on the fact that, under the above assumptions, the line of propagation (LOP) of the magnetic field can be extracted from the sensor data. A LOP is the line connecting the (center of the) magnetic field source to the measurement position, as projected on H. In principle, with a fixed source, if we can extract from the measured data two different LOPs, their intersection gives the source position. In order to obtain two different LOPs, at least two magnetic field measurements, taken in two different positions in the environment, are needed. Let us consider a generic spatial position P in the environment. If the three spatial components of the magnetic field in P were available, the calculation of the LOP passing through P would be trivial. Unfortunately, the sensors we used do not output these spatial components: they output instead the modulus of their DFTs, but not the associate phase information. This implies that for each position P two possible LOPs (plops) are generated. One of them is the actual LOP, while the other one is an artifact of the generation process. With the data from a single measurement it is not possible to choose

between them. The algorithm for plops extraction is described in detail in [3], along with a description of the possible special cases. To determine the actual source position our localization algorithm uses the data returned by three measurements. These must be taken at three different positions in the environment because, if the source is stationary, successive measurements performed in the same position would lead to the same plops. After the first two measurements, we obtain a set of 4 possible source locations (psls). These psls are the intersection points between the 4 plops returned by the two measurements (Figure 2). Then, a third measurement is used. This also gives two plops and, since the source must belong to one of them, one of these new plops must pass through one of the 4 psls. So, checking the distance between the new plops and the 4 psls, we can identify the actual source position among the psls. If none of the new plops pass through one of the psls, it means that the source has moved and the three measurements refer to different source positions. There are degenerate cases where the relative locations of the three measurement positions do not allow source localization (for details see [3]): in these cases more than three measurements are required to complete the localization process. y P 1 psl P 2 x Figure 2. Possible source locations after two measurements in positions P 1 and P 2 The system is presently configured to use the last three available measurements to locate the source, in order to quickly react to source movements. However, it is possible to use more measurements to improve the accuracy of source localization. Once the position of the source is known, it is trivial to find the cell corresponding to the source in the grid map of the environment. 3. Some Experimental Results In this section, we present some of the experiments we performed to validate our robotic sensor network. In particular, first we discuss an example of operations performed by the system and, second, we illustrate some performance results. In the first experiment, we show a test with a moving EMF source. We used simulated EMF sensors that return data in accordance with the position of a simulated EMF

Figure 3. The explorers move toward a EMF source changing its position source placed in the environment via a graphical interface. As our simulated sensors give complete information about the magnetic induction field vector, only two measurements are needed to determine the source location, instead of the three needed with the real sensors. In detail, the following activities have been performed (see Figure 3): the explorers performed an EMF measurement in their initial locations; the coordinator used the data gathered by the explorers to determine the estimated position of the EMF source (represented by the white trash bin, left top of Figure 3); the coordinator generated a task that required to perform EMF measurements around the source position; after having negotiated the assignment of the task, one of the explorers (the closest one) moved toward the cell where the EMF source was estimated to be (left bottom of Figure 3): each time the robot reached the center of a cell along the path, it executed an EMF measurement (these data were used by the coordinator to confirm the hypothesis about source position); we manually changed the position of the EMF source in the the environment (right top of Figure 3); after one of these measurements the coordinator reacted to the change of the EMF source by changing the hypothesis and identifying a new task; after having negotiated the assignment of the new task, the second explorer (closer than the first one) moved toward the cell where the EMF source was now estimated to be (right bottom of Figure 3). We also performed a number of experiments to evaluate the performances of our robotic sensor network with real magnetic field sensors, using a simple single-frequency magnetic field generator. We built this test source using 200 m of electrician wire, tightly wound in a cylinder approximately 20 cm in diameter and 15 cm in height, with a central

Figure 4. Source location (the square at (2100, 2100) represents the center of the source, units are mm) and estimated source positions (diamonds) hollow with a diameter of approximately 8 cm. The axis of the cylinder was orthogonal to the floor plane. We connected this device to the 230 V 50 Hz power line, in series with a 500 W halogen lamp used as resistive load. We tested the effective localization of the EMF source provided by the system. We put the center of the EMF source at coordinates (2100, 2100) (units are mm) and performed its localization by randomly generating measurement positions until localization was possible. Recalling previous section, this usually requires three measurement positions; but when the relative location of source and measurement positions leads to a degenerate case or signals are too weak, additional measurements are required to complete the localization process. Measurement positions for the explorers have been generated within a distance of 2 m from the source. We repeated the test 15 times. Each test was stopped when the system produced an estimated source position. Results are shown in Figure 4 (left). The position of the source has been determined with good precision in almost all tests. Only in one test the source has been localized with a precision worse than 40 cm (recall that the diameter of our source is 20 cm). In 13 tests, the source has been localized after with three measurements, according to what discussed in Section 2.2. In the other 2 tests, four measurements have been required to disambiguate source position. We report another experiment we have conducted in order to validate the reliability of the localization of the EMF source provided by the system. Also in this case, we put the EMF source at coordinates (2100, 2100) but we performed its localization by randomly generating 9 measurement positions. In this way, by using a number of measurements larger than the minimum required for the localization, we could validate the robustness of the localization. Measurement positions for the explorers have been generated within a distance of 2 m from the source. We repeated the test 8 times. In principle, with 9 measurements, each test should produce 7 estimated source positions (because localization cannot be done when only the first two measurements are available), and, over all the tests, we should have 56 estimated source positions. However, we could get only 53 estimated source positions, because in two cases weak and disturbed signals prevented the localization of the source and in another case a further measurement was required to disambiguate source position. Results are shown in Figure 4 (right). The position of the source has been determined with good precision; only in a dozen of cases the source has been localized with a precision worse than 40 cm (recall that the diameter of our source is 20 cm). We averaged the estimated source positions obtained in the single tests, obtaining 8 average estimated source positions that are shown in Figure 5.

A final remark is on our localization algorithm. Since it works by considering the last three measurements, the algorithm can easily track moving sources (at least when this movement is slow compared to that of the robots); however, the algorithm does not increase the precision of the estimated source position over time because it does not take into account all the previous measurements. Figure 5. Source location (the square at (2100, 2100) represents the center of the source, units are mm) and average estimated source positions (diamonds) 4. Conclusions In this paper we have presented a multirobot system devoted to EMF monitoring. The system is a robotic sensor network that can autonomously deploy its explorers in an environment to cope with dynamic events, like a moving EMF source. Despite some features of our system have been specifically designed for the application we tackled, some architectural solutions (such as the contract net for assigning tasks to explorers) are quite general and could be easily reused in other application domains. The importance of robotic sensor networks is expected to increase in the near future, due to the technological advances in miniaturization and communication. One of the most interesting challenges for the next generation robotic sensor networks will be the capability to address more tasks at the same time: for instance, the simultaneous monitoring of multiple environmental phenomena and map building. Acknowledgements This work is part of the APE Project funded by the MIUR (Italian University and Research Ministry) in 2002-2004. References [1] F. Amigoni, A. Brandolini, V. Caglioti, V. Di Lecce, A. Guerriero, M. Lazzaroni, F. Lombardi, R. Ottoboni, E. Pasero, V. Piuri, O. Scotti, and D. Somenzi. Agencies for perception in environmental monitoring. IEEE Transactions on Instrumentation and Measurements, 55(4):1038 1050, 2006. [2] F. Amigoni, A. Brandolini, G. D Antona, R. Ottoboni, and M. Somalvico. Artificial intelligence in science of measurements: From measurement instruments to perceptive agencies. IEEE Transactions on Instrumentation and Measurement, 52(3):716 723, 2003.

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