Infrastructure Monitoring With Multi-Robot Teams - ISR-Coimbra

Infrastructure Monitoring With Multi-Robot Teams Gonc¸alo Cabrita, Pedro Sousa, Lino Marques and An´ıbal T. de Almeida

Abstract— This article presents the first steps toward an automatic indoor environmental monitoring system through the use of a group of mobile robots. A metric map is provided a priori to the robots, ensuring the navigation, localization and extraction of points of interests for patrolling. The communication layer is robust and allows any robot to enter or leave the patrolling task. The monitoring of the environment is achieved by acquiring environmental information during the patrolling. If an abnormal condition is detected, the system should react, providing a fine coverage of the suspicious area and an accurate identification of its source. Experiments were conducted inside a building and data was gathered and post processed. The yet preliminary results demonstrate the effectiveness of the method employed to control the system, showing good area coverage from the patrolling algorithm and acceptable representations of the collected variables (alcohol concentration and temperature).

I. INTRODUCTION Environmental monitoring can be described as the process of collecting the necessary data to characterize the quality of the environment. This notion can be applied to countless indoor or outdoor applications. A wide range of Man-built structures accommodate chemicals potentially dangerous to both its occupants and Nature. Furthermore public buildings such as airports or train-stations have become preferential targets for terrorist attacks. The timely detection of environmental anomalies in this scenarios can prevent potential disasters and the consequent life losses and property damage [1]. Other structures might hold animal or plant life that needs certain environmental conditions in order to be properly sustained. Activities like agriculture can benefit from such technologies. In museums all over the world variables like temperature and humidity must be kept constant at all times in order to help preserve the art pieces which continuously struggle against time [2]. Environmental monitoring is usually achieved by means of a sensor network. The network nodes can be static, mobile or a combination of both. Static wireless sensor networks (WSN) consist of small nodes equipped with sensors capable of measuring the desired phenomena. The available solutions are usually cheap and easy to deploy, even over large areas, both indoors and outdoors. Static WSN have found their way into many monitoring applications, from museums [3] to large glaciers [4]. Mobile robots equipped with multiple sensors can create a mobile WSN. Mobile robot platforms come in many shapes, from small ground robots to unmanned G. Cabrita, P. Sousa, L. Marques and A. T. de Almeida are with Dept. of Electrical and Computer Engineering, Institute of Systems and Robotics, University of Coimbra, 3030-290 Coimbra, Portugal.

{goncabrita, pvsousa, lino, adealmeida}@isr.uc.pt

aerial vehicles (UAVs) or even underwater unmanned vehicles (UUVs), allowing for their deployment in almost any scenario. A mobile WSN will ultimately perform the same task a static WSN would, however a small number of mobile sensors is able to achieve a similar spatial resolution to that of a static WSN installed over a larger area. Furthermore a team of robots can be deployed virtually anywhere in a short amount of time, hence being a far more flexible solution [5]. Finally some applications can benefit from the use of both static and mobile WSNs. Mobile robots deployed within an environment equipped with a static WSN can tap into the existing network to access the environmental data of the covered area. This allows the robot to make decisions based on this data and get more detailed readings, thus improving the coverage and spatial resolution of the complete system [5]. Patrolling can be defined as the task of repeatedly visiting a desired location with the purpose of assessing certain aspects of its environmental state. Since it is not possible to cover all space at all times each point is visited once every T seconds, thus the frequency of patrolling is defined as 1/T Hz [6]. Also known as sweeping or repetitive coverage, the task of patrolling has had considerable attention from the mobile robot community in the past few years. Solutions for multi-robot scenarios usually approach the problem by dividing the area to patrol into sub-areas which are then assigned to the available robots. Once this is done each robot will patrol its own sub-area by means of a singlerobot patrolling algorithm [6]. In 2002 Machado presented a discussion of multi-agent patrolling task issues. Several architectures were then compared, from which the best strategy was considered to be Conscientious Reactive, which is a local and reactive strategy with no communication, based on individual idleness and without central coordination [7]. Later in 2004, Chevaleyre carried out a study focused on two graph-theory centralized planning strategies: cyclic strategies and partitioning strategies [8]. Elmaliach studied the problem of generating patrolling paths for a team of robots and presented it in [6]. The patrolling algorithm presented guarantees that each point in the target area is covered at the same optimal frequency through the use of Hamilton cycles. More recently, Portugal and Rocha developed the Multilevel Subgraph Patrolling (MSP) algorithm [9]. This algorithm was proven to be superior to existing patrolling algorithms. Chemical variables can be monitored by a static sensor network or by mobile robots equipped with monitoring devices. The most common types of chemical variables monitored are: volatile organic compounds (VOCs), air contaminants, and other type of toxic or hazardous gases [10],

[11], [12]. This type of chemical monitoring can have multiple objectives from simple air quality supervision to contamination level tracking or even to trigger alarms in the case of a serious abnormal occurrence (such as a poisoning gas leak or other accident). In the past few years, several researchers have studied the ability of a mobile robot to react to chemical cues and track those cues until their source. This problem, known as the odor plume tracking problem, has been addressed with several different approaches, either in indoor or in outdoor environments. Marques [13] compared the performance of a gradient-based strategy with two bio-inspired strategies (moth surge/zig-zagging/spiraling, and bacteria biased random walks) tracking two different odor sources in a turbulent environment. In a recent work, Lochmatter [14] compared the tracking ability of three bio-inspired algorithms in a laminar wind field. Other researchers employ dense coverage approaches [15] or nave physics rules to estimate the localization of odor sources indoor [16]. The rest of this paper is organized in the following order: Section II states the problem presented in this paper followed by Section III where the proposed methodology for solving the problem is explained. The experiments performed through out the project are described in Section IV while the results and discussion can be found in Section V. Finally Section VI presents both the conclusions and future work. II. PROBLEM STATEMENT The goal of this work is to be able to monitor a known indoor environment. It is assumed that a given number of robots, equipped with sensors able to measure the variables of interest and to communicate with each other are available. Each robot should be able to navigate inside the facility, patrolling and detecting anomalous environmental conditions (e.g., a high concentration of a chemical vapor). When this happens, the source of the problem should be identified, either by the detecting robot or jointly with the support of other robots. In the current work only chemical releases were considered and the problem can be divided in the following sub-problems: (1) patrolling and (2) odor source localization. The patrolling stage begins with the assignment of patrol sub-areas to each robot, after which each robot will patrol its designated area. It is assumed that a metric map of the facility is provided to the robots. For a flexible and robust system, robots joining or leaving the communication network should lead to a redistribution of the patrolling areas thus providing a higher patrolling frequency given a higher number of robots or not allowing a certain area to remain un-patrolled in the event of a robot malfunction. The odor source localization stage is triggered when a robot detects an abnormal level of any of the chemicals it is able to sense in the environment. In order to perform a multi-robot odor source localization algorithm the necessary number of robots should be mobilized. Upon the detection of the odor source a robot should remain to mark the spot. The following section provides an overview of the proposed methodology for solving the problem stated earlier.

III. PROPOSED METHODOLOGY A. Map Representation A task such as multi-robot patrolling, with the exception of simpler reactive algorithms, inherently needs a map. Maps usually come in two flavors, metric and topological. Metric maps provide a realistic representation of the environment, on the down side they are heavy and complex to process by most algorithms. Topological maps store less information by comparison and as a consequence can be used much more efficiently by most algorithms. For this project both types of maps were used. A metric map of the infrastructure to be patrolled is used for navigation and localization. The same map is converted into a topological map using OpenCV. The discrete Voronoi diagram is extracted from the metric map, from which the points of interest are obtained. The topological map is necessary to perform the patrolling algorithm. B. Communication Although it is not mandatory, communication is useful when using a group of robots. Communication can be centralized or distributed. Distributed communication is more robust since it does not depend on a server to operate properly. This allows for any part of the system to be disconnected without compromising the integrity of the group. A communication node was developed by the researchers involved in this project. It relies on OLSR1 to monitor robots connecting and disconnecting from the network. Olsrd is the implementation of the Optimized Link State Routing protocol (OLSR) which provides mesh routing for network equipment. Olsrd is an ongoing open source project, fast, highly portable (runs on multiple platforms) and scalable (runs on community wireless mesh networks with several nodes) routing protocol for mobile ad-hoc networks [17]. The developed communication node also allows for the exchange of messages between different robots without the need of a server, allowing for any part of the system to malfunction without compromising the integrity of the whole system. C. Patrolling Algorithm The MSP algorithm presented in [9] was chosen for this project. The MSP Algorithm assumes that robots are endowed with the environment map and the ability for self-localization and navigation. The algorithm is based on multilevel partitioning of the environment map, assigning different regions to each mobile agent. Each region corresponds to a sub-graph extracted from the existing topological representation. The algorithm deals, then, with effectively patrolling each region by computing paths for every robot in the assigned sub-graph. To accomplish this, it searches each sub-graph using a classical algorithm for Euler cycles (visit each edge exactly once) and various heuristics for Hamiltonian cycles (being a closed loop and visit each region exactly once), non-Hamiltonian Cycles (do not satisfy 1 http://www.olsr.org/

Fig. 1.

Odor source (cross) and the chemical plume in a junction.

Hamiltonian conditions) and longest paths (a path with the maximum length in the given sub-graph). D. Navigation and Localization

Fig. 2.

The Roomba robots.

As stated earlier, a metric map was used for navigation and localization. This is accomplished through the navigation stack from ROS2 (further addressed in Section IV). The navigation stack takes information from the robot odometry and from a laser range finder, and outputs to the robot platform safe velocity commands that allow reaching a target positions [18]. The adaptive Monte Carlo localization approach [19], which uses a particle filter to track the pose of a robot against an existing map is present in the navigation stack, providing robot localization in the known environment. This allows the robot to visit the points of interest provided by the patrolling algorithm described previously. E. Monitoring the Environment Monitoring the environment parameters is a complex and difficult task. In this work, mobile robots are programmed to patrol a certain area while acquiring various environmental parameters. Each node of a given path can be visited with a homogeneous frequency or with a frequency dependant with its expected interest (e.g., the expected probability of observing an accident or abnormal occurrence). This frequency depends from the robot path, so the path of each robot should be optimized in order to obtain complete area coverage with a frequency of visits as close as possible to the desired one. The robot can detect several items, namely: general pollutants (e.g., CO2 , CO, N Ox , SO2 , VOCs) or specific chemical substances (e.g., hydrocarbons, ammonia, or other poisoning gases), particles, temperature or humidity, among others. Basically, it tries to create a site characterization where its localization along with the acquired parameters (alcohol and temperature in our experiments) can be shown. The spatio-temporal data acquired by each robot is sent to a central station where all the relevant variables are fused in order to construct real-time risk maps of the whole environment. While performing the patrolling task, whenever an abnormal reading occurs, an alarming event should be triggered (to assess and address the information received) and a source tracking behavior should be started. 2 Robot

Operating System - http://www.ros.org/wiki/

Fig. 3.

Chemical sensor used in the experiments.

F. Odor Source Localization In the current implementation the robots are not measuring airflow intensity and direction. This constraints the odor plume tracking algorithms that can be employed, since the most effective way to find an odor source consists in moving upwind through the plume. Given this constraint and the type of environment that was used in the experiments (mostly corridors, junctions and rooms), a discrete search across the nodes and some heuristics was employed to move across the corridors containing the chemical vapor of interest and estimate its source. As can be seen in Figure 1, when a chemical trace is detected by the robots, there are a limited number of branches from where it can come up and therefore a small number of alternatives for the odor source localization. With a directional anemometer, it would be possible to explore immediately the most probable branch. The team is currently working in the integration of directional anemometers and in the implementation of efficient odor plume tracking and localization. IV. EXPERIMENTS For this project a small group of Roomba robots depicted in Figure 2 was used. The Roomba is manufactured by

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Nodes in the map (numbers) and the release point (A).

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Fig. 6.

Robot paths and the chemical data acquired by the robots.

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The chemical release mechanism. 55

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iRobot as an autonomous vacuum cleaner, however it is possible to access and control the robot. This provides a cheap yet robust platform, ideal for multi-robot experiments. Each robot is equipped with a small Eee PC running ROS, an Hokuyo laser range finder, a small artificial nose (shown in Figure 3) and a thermopile array - TPA81 - from Devantech (used for temperature acquisition). ROS is an open-source operating system for robots. It provides the services expected to be found in an operating system such as hardware abstraction, low-level device control or message-passing between processes [20]. Real world experiments were carried out in the corridors of the Institute of Systems and Robotics (ISR) in the University of Coimbra. The initial experiments did not include real chemical releases, instead PlumeSim was used to simulate a plume in the corridors of ISR. PlumeSim is an odor plume simulator designed by the researchers involved in this project [21]. This allowed to fine tune the algorithm before releasing actual chemicals into the somewhat crowded corridors of ISR (Figure 4) for the final experiments. The final set of experiments included a heat source and the setup of Figure 5 capable of releasing alcohol into the environment. The location of the heat source and the chemical release mechanism is represented as point A in Figure 4. Each of the three robots was deployed at their starting locations, and the corridors were patrolled for 30 minutes during each experiment, during which time both the chemical and temperature data were monitored. V. RESULTS AND DISCUSSION The MSP algorithm provided the nodes shown in Figure 4. The resulting patrolling paths were: robot one 1-2-3-2-42-5; robot two 6-5-7-5-8; robot three 9-8-10-8-11. After

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Fig. 7. robots.

Robot paths and the temperature measurements acquired by the

concluding a patrol cycle the robots can either go back to their first node or perform the inverse path. The inverse path was chosen for this project. The chemical and temperature data collected during the patrolling with real chemical release is here represented in the form of spatial-temporal graphics. Figure 6 shows the results for the chemical data while Figure 7 shows the results for the temperature data. Axis x and y represent the position of the robot throughout the experiment in meters, the zaxis represents time in seconds and the color represents the chemical concentration or temperature. Both the chemical (Figure 6) and temperature (Figure 7) data indicate that after the robot has passed point A in Figure 4 the values read by the sensors take a considerable travelled distance to come back to clean air concentration and room temperature. This can be explained by the relatively fast speed imposed on the robot to assure that a satisfactory patrolling frequency is achieved and by the time that the sensors take to settle back after being exposed to high chemical concentrations or high temperatures. This is also

R EFERENCES

Fig. 8.

The Roomba robots during patrolling task.

clear by the fact that for example on Figure 6 none of the other robots sense the chemical even when close to nodes 5 (robot 1) and 8 (robot 2) show in Figure 4. Nonetheless the robot is capable of pinpointing the point of chemical release, a maximum of chemical concentration is perfectly visible in Figure 6 coincident with point A in Figure 4. On the other hand the robot was not able to pinpoint the same point for the heat-source, indicating that a more suitable temperature sensor should be used for the purpose of this experiment.

VI. CONCLUSIONS AND FUTURE WORK The presented work represents another step towards practical real-life implementation of recent advances in the field of mobile robotics in the area of environmental monitoring. The initial results are satisfactory as the group of robots is able to perform the patrolling and monitoring tasks as expected. The focus of this work will now go to the multi-robot odor source localization algorithm. Further experiments will also be performed with a larger group of robots to determine the robustness and flexibility of the system as a whole, determining how the group of robot reacts to new robots or malfunctioning robots and how the patrolling routes are rearranged as needed. Moreover the temperature sensor proved to be inadequate for the purpose of this project, as a result a new temperature sensor will be procured for the future, along with an humidity sensor (or even a combination of both), relevant for many of the applications described early in the paper. Since the developed work is modular it is possible to easily integrate future advances in the fields of patrolling and odor source localization, hence improving the final result of this project. Future work could also include an initial stage of autonomous mapping, which would allow the robots to be deployed in an unknown environment. If this was the case providing the robots with a map of the facility would not be necessary. Furthermore, if the robots were able to recharge by means of charging stations it would be possible to maintain robots patrolling the target facility indefinitely.

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