Multi-robot olfactory search in structured environments

Robotics and Autonomous Systems 59 (2011) 867–881

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Robotics and Autonomous Systems

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Multi-robot olfactory search in structured environmentsAli Marjovi ∗, Lino MarquesInstitute of Systems and Robotics, University of Coimbra, Portugal

a r t i c l e i n f o

Article history:Received 6 February 2011Received in revised form20 July 2011Accepted 25 July 2011Available online 2 August 2011

Keywords:Olfactory searchTopological explorationOdor source localizationSearch in structured environments

a b s t r a c t

This paper presents a cooperative distributed approach for searching odor sources in unknown structuredenvironments with multiple mobile robots. While searching and exploring the environment, the robotsindependently generate on-line local topological maps and by sharing them with each other theyconstruct a global map. The proposed method is a decentralized frontier based algorithm enhancedby a cost/utility evaluation function that considers the odor concentration and airflow at each frontier.Therefore, frontiers with higher probability of containing an odor source will be searched and exploredfirst. The method also improves path planning of the robots for the exploration process by presenting apriority policy. Since there is no global positioning system and each robot has its own coordinate referencesystem for its localization, this paper uses topological graph matching techniques for map merging. Theproposed method was tested in both simulation and real world environments with different number ofrobots and different scenarios. The search time, exploration time, complexity of the environment andnumber of double-visitedmap nodes were investigated in the tests. The experimental results validate thefunctionality of the method in different configurations.

© 2011 Elsevier B.V. All rights reserved.

1. Introduction

Search and rescue operations inside buildings, caves, tunnelsand mines can be extremely dangerous tasks. An example ofsuch an extremely risky situation is human operation insidean industrial warehouse during a fire. In these cases, humansenses can become severely impaired: smoke reduces visibility,communication is rendered impossible by the noise caused bythe fire, and additionally, dangerous vapors and toxins may bereleased. The use of autonomous robots to assist such tasks reducesthe risks involved in these operations. Robots can search fortoxic chemicals and other desired targets while they explore theenvironment, providing real-time data about the discovered mapand the status of the facility.

An air scenting Search andRescue (SaR) dog is especially trainedto locate the scent of any human in a specific area and get close tothe source of the scent; they can do so from hundreds of metersaway, in heavy bush or in the dark. Handlers working with SaRDogs are very much aware of surface winds. They position theirdogs downwind from all portions of their assigned sector duringthe search. Future robots equipped with olfactory sensors canpotentially preform the same task in SaR applications.

This study was integrated in a European project namedGUARDIANS1 and its main goal was to develop a group of au-tonomous robots to navigate and search urban environments. The

∗ Corresponding author. Tel.: +351912839734; fax: +351 239 406 672.E-mail addresses: [email protected] (A. Marjovi), [email protected] (L. Marques).

1 http://www.guardians-project.eu/.

0921-8890/$ – see front matter© 2011 Elsevier B.V. All rights reserved.doi:10.1016/j.robot.2011.07.010

GUARDIANS central example was a search and rescue mission inan industrial warehouse (similar to Fig. 1) containing smoke. Therobots should search for toxic chemicals (odor sources) and gener-ate a map of the building while providing information for the fire-fighters.

1.1. Problem statement

Consider a group of N mobile robots, moving in R2 that arelabeled as R1, R2, . . . , RN . Each robot Ri (i = 1, . . . ,N) is ableto communicate with the other robots located at a short distance∆. The robots are equipped with sensors for measuring theodor concentration and air flow direction. There are unknownnumber of odor sources in the area which emit odor gas into theenvironment. There is no central base-station for the system, sothe robots should act separately and independently from eachother. There is no global localization system and the robots’odometry is not very accurate. There is no prior knowledge aboutthe environment except that it is a structured building similarto a warehouse containing corridors, corners, branches, crosses,etc. (Fig. 1). The problem is to localize all odor sources in theenvironment, explore the whole area, and generate a map of theenvironment.

1.2. Challenges

Efficient search and exploration in unknown environments isa fundamental problem for mobile robotics. As this problem

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868 A. Marjovi, L. Marques / Robotics and Autonomous Systems 59 (2011) 867–881

Fig. 1. Robots searching in a warehouse.

becomes increasingly solved by single robots, especially in obstaclefree environments, the next problem is to extend these techniquesto groups of robots on structured environments. Using multi-robot systems may potentially provide several advantages oversingle robot systems, namely faster exploration, better accuracy,and system fault tolerance. However, in addition to the problemsoccurring in single robot olfactory search and exploration, theextension to multiple robots poses following challenges:

• Multi-robot olfactory searching.• Multi-robot task sharing and cooperation.• Localization and multi-robot mapping.

1.2.1. Multi-robot olfactory searchingMost of the related works concerning olfactory search have

focused on either single robot experiments [1–3] or multiplerobots operating in open areas free of obstacles [4–6] with abackground fluid flow. The odor is carried downwind originatingfrom the source forming a plume. Due to turbulence the chemicalconcentration within the plume is patchy [7].

Researchers have developed methods that employ combina-tions and variations of plume acquisition [8,9] and plume upwindfollowing [5,8,10] using reactive control algorithms (a compari-son of these methods is in [11]). Most of these approaches are in-spired by very simple creatures like moths [3], glowworms [12],etc., therefore they are mostly low-level algorithms making therobot react to the environmental changes based on some definedrules. Despite most of these works, this paper needs to fulfill thegoal with higher level of cooperation of multiple robots in realstructured environments. How can a group of robots efficiently lo-calize odor sources in structured buildings? This is a challenge thatshould be more studied in the robotic community.

1.2.2. Multi-robot task sharing and cooperationCooperation is a key parameter in the performance of a group

of robots which are trying to solve a general search problem.The more robots that search an environment, the more importantthe coordination between their actions becomes. The cooperation,communication, and management of the robots in a multi-agentsystem can be done either in centralized way by using a basestation as the server, or decentralized way by having a distributedbehavioral based method (like in [13–15]). This study tackles thesearching problem in unknown environments without using acentral station. The lack of a central station makes it more difficultto distribute the tasks between the robots. Since the environmentis unknown, the robots are unaware of the tasks before searching

Fig. 2. Multi-robot frontier-based search and exploration.

the area, i.e. there can be no kind of task allocation before the startof the mission. Task allocation must be done automatically duringthe mission by the participating robots.

The problem statement in this paper explained that thereare unknown number of odor sources in the environment andthe robots should localize all of them. Since the robots haveno a priori knowledge about the number of odor sources theymust operate in the environment until they cover all the area,i.e. they must continue searching while there are still unexploredareas. Therefore this search problem should be complimentedwithunknown environment exploration problem.

Singh and Fujimura [16] presented a decentralized onlineapproach for heterogeneous robots. In their method the robotswork independently most of the time. When a robot finds asituation that is difficult to solve by itself, it sends the problemto another robot which may be able to solve the situation.The candidate robot is chosen by trading off the number ofareas to be explored, the size of the robot and the straight-linedistance between the robot and the target region. This techniquegenerates a grid geometric map; therefore, the accuracy of themap depends on the grid size. Moreover, all the robots need tohave a considerable amount of memory to store the entire map.Yamauchi [17] proposed a distributed method for multi-robotexploration, yielding a robust solution even with the loss of oneor more vehicles. A key aspect of that approach involves sharingmap information among the robotic agents so that they executetheir own exploration strategy independently of all other agents.The robots move to the closest frontier2 according to the currentmap, however, there is no coordination component which choosesdifferent frontiers for the individual robots.

If the robots know their relative locations and share amapof theexplored area, then effective coordination can be achieved throughguiding the robots into different, non-overlapping areas of the en-vironment. In other words, effective coordination can be achievedby extracting exploration frontiers from the partial maps and as-signing robots to frontiers based on a global measure of perfor-mance [14,18]. Frontier based exploration is a simple approach fordecentralized multiple robot task allocation (Fig. 2). These fron-tiers, thus, represent locations that are reachable from within thepartial map and provide opportunities for exploring unknown ter-rain, thereby allowing the robots to greedilymaximize informationgain [19]. However, these methods should have a strategy to avoidsending two robots toward the same frontier.

Most of the related works in this area (namely [18,14]) tryto explore the environment with minimal excess effort in theminimum possible time, however, the main goal of this project isolfactory search rather than exploration. The exploration must bedone in such a way that the group of robots automatically intendsto look for the odor sources in the environment.

2 Frontiers are the borders of the partial map, between explored space andunexplored area.

A. Marjovi, L. Marques / Robotics and Autonomous Systems 59 (2011) 867–881 869

1.2.3. Localization and multi-robot mappingWhile multiple robots cooperatively search and explore an en-

vironment, information from individual robots must be integratedto produce a single globally consistent map. This is a difficult prob-lem to solve when the robots do not have a common referenceframe or physical global positioning system [20]. Most of the ex-isting approaches to coordinate multi-robot mapping assume thatall agents know their locations in a shared (partial) map of the en-vironment [13,14,18]. Having a physical general positioning sys-tem is an undesired constraint in unknown areas where there is noprevious knowledge about the environment. Simultaneous local-ization and mapping (SLAM) has been a topic of much interest be-cause it provides an autonomous vehicle with the ability to discernand represent its location in a feature rich environment. Some ofthe statistical techniques used in SLAM include extended Kalmanfilters, particle filters (Monte Carlo methods) and scan matchingof range data [21]. However, in the context of metric map build-ing, SLAM’s performance depends on the accuracy of the environ-mental sensors and requires very high data processing and alsocommunication between the robots. This work uses the conceptsof topological SLAM [22] for creation of globally consistent mapsof structured environments in real time within the computationallimits of available hardware.

Topological maps provide a brief characterization of the navi-gability of a structured environment, and, with measurementseasily collected during exploration, the vertices of the map canbe embedded in a metric space [20]. These maps use a graphto represent possibilities for navigation through an environmentand need less memory than their metric counterpart. Theproposed approach employs a topological mapping technique,so the robots only exchange few environmental features. Usingtopological maps, the problem of ‘‘map merging’’ is reduced to a‘‘graph merging’’ problem [20,23]. Whereas most approaches totopological map merging and related problems have focused onusing either map structure or map geometry [24], the proposedalgorithm in this paper takes advantage of both. Similar to [20],the use of map structure allows quick identification of potentialvertex matches in the maps (and rejection of mismatches), whilethe use of map geometry enables the algorithm to directly mergemaps with multiple (disconnected) overlapping regions. However,if the geometric data of the local maps are not obtained through aunique coordinate system these methods are not functional. Thispaper deals with two issues concerning map merging, the first,dealing with the uncertainty in the localization of each robot bycorrecting it using the information of the partial maps, and thesecond, presenting an approach for map merging for the casein which the robots’ coordinate systems are different from oneanother.

The main contribution of this paper is taking olfactory cluesinto the decision making of exploration of unknown structuredenvironments with multiple robots. The proposed method is adecentralized frontier based algorithm enhanced by a cost/utilityevaluation function that considers the odor concentration andairflow at each frontier so that the robots will try to find the odorsources as fast as possible. The researchers who have presentedcooperativemulti-robot approaches in the field of olfactory search,have not addressed the problem by a frontier-based search andexplorationmethod (maybe one of the reasons is thatmost of themdo not consider the problem in structured environments and withmultiple robots). On the other hand, the researcherswhohave beenworking on multi-robot unknown environment exploration havenot addressed the problem of olfactory search. We believe that forsearching olfactory sources in unknown structured environmentsby a team of robots, modified exploration methods can practicallyaddress the problem.

In our previous studies [13,18], techniques for multiple robotsthat intended to explore an unknown environment and generateits topological map were presented. In those studies the robotsexplore the whole environment but the current research tries tosearch the environment based on the odor concentration that isreported in each frontier while at the end the whole environmentwill be explored. ‘‘Cooperation’’ is done by sharing and integratingthe local robots’ information into the topological map. Each robottransfers its own local data (including its aimed target) to theother robots. In this way the whole group is aware of the exploredenvironment and therefore is able to make future decisions basedon that information. Moreover, the robots maintain a policyof avoiding collisions between each other. ‘‘Task sharing’’ isperformed based on the following policy; the robots automaticallypick up the tasks such that the unexplored frontierwith the highestodor concentration will be assigned to the nearest idle robot. Interms of ‘‘map merging’’ this paper proposes a method based onsubgraph isomorphism that works even if the robots do not havethe same reference coordinate systems.

2. The proposed method

It is desired to find odor sources and explore the wholeenvironment as fast as possible. Therefore, it is essential that therobots share their tasks and individually achieve the objectivesthrough optimal paths toward the odor sources. In an unknownenvironment, the immediate goals are the frontiers. while therobots are exploring an area, there are several unexplored regions,which poses a problem of how to assign specific frontiers to theindividual robots without the existence of a specific task allocator.In the proposed approach, the robots firstly decide to explore thefrontiers which indicate higher odor concentration. The robotsmust avoid selecting the same frontier, this may result in collisionconcerns. Another problem is the lack of base station, so the robotsshould be able to explore autonomously and also avoid collisionsbetween themselves. To address these problems, the proposedmethod is based on a behavioral decision-making explorationstrategy that is shown in Algorithm1. The flowchart of thismethodis depicted in Fig. 3.

2.1. Odor source search and exploration algorithm

Algorithm 1 describes the decision making technique thatshould be run on every robot. The robots start exploring the envi-ronment independently. Each robot goes forward to get into newfeatures in the environment. It generates its own local topologi-cal map out of the detected features of the environment and alsotransmits this local map to the other robots which are working onthe same environment. When a robot has to make a decision toselect its future path (e.g. in the branches), it first measures odorconcentration in that place. If the odor concentration is more thana certain threshold, it means that the robot is in an odor plumeand it must go up-wind direction in order to localize the source.However if a robot is traveling inside an already explored area andwants to select a frontier to explore, the frontier should be selectedbased on the cost of reaching it and the utility it can provide to thesearch. The cost is calculated through the A* method [25], where itsimultaneously determines the optimal path to reach the frontierand its distance. Therefore, the cost is proportional to the distancethat the robot has to pass to reach the frontier.

cost(i, R) = dist(A∗

i=0,n[(XR, YR), (Xfi , Yfi)]) (1)

where:

(XR, YR) → position of the robot R(Xfi , Yfi) → position of the frontier in → number of frontiers.


Fig. 3. The flowchart of the proposed method.

The utility depends on the level of odor concentration in thatfrontier, which means that if there are several frontiers at similardistances, the robot will go to the one that has higher utility,i.e. higher odor concentration.

∀i ∈ {1 . . . n}utility(i) = Odor_Concentration(i). (2)

The profit of a frontier i for a robot R is then calculated as

profit(i, R) = utility(i) − β × cost(i, R) (3)

where β represents a coefficient representing the relative valuesof cost and utility.

Since each robot always tries to maximize the profit function inits frontier selection, the frontiers with higher odor concentrationwill be explored faster by the robots which aremore close to them.Fig. 4 shows a snapshot of three robots searching in a structured

Algorithm 1: Odor source search and exploration algorithm

// Definitions:1// feature: features are corridors, corners, etc. in a structured environment.2// node: a node represents an environmental feature in the topological map.3// frontier: an unexplored link in a node in the map.4begin5

while there is at least one frontier in the map do6repeat7

Go forward and follow potential field algorithm()8Path planning improvement() // explained in Section 2.49Feature extraction() // explained in Section 2.210

until getting a different environmental feature;11C = Measure odor concentration()12W = Detect wind direction by anemometer()13if current node exists in the map then14

Update map’s data()15Localization correction() // explained in Section 2.616

else17Add current node to map()18

if (M >odor_threshold) and (W is not explored) then19Set Objective to go(W )20

else21F = unassigned frontier with highest Utility − Cost()22Assign frontier F to this robot() // in the map23D = the best path based on the A∗ algorithm(F )24Set the new Objective to go(D)25

if Odor source detected() then26Report this place as an odor source into the map()27

Send the new local map to the other robots()28if received map data from other robots then29

Map Merging() // explained in Section 2.530

end31

Fig. 4. 3 robots searching in a small-scaled structured environment.

environment. The already explored area is highlighted and thefrontiers are colored by green. The robots select frontiers based onthe explained algorithm.

During exploration and navigation, the robots are simultane-ously acquiring olfactory information (odor concentration and airflow direction) of the environment. While a robot is traveling up-wind direction, if the level of detected gas decreases suddenly itmeans that the robot has passed an odor source during its path. Onthe other hand, if a robot is traveling in the down-wind directionand it starts to detect a high concentration of odor, it means thatthere is an odor source in this location.

The robots generate the topological map of the environmentduring their search and exploration mission. Within the topolog-ical map, besides having information regarding the kind of nodes,their position and the odor concentration in that feature, it also hasdata describing the location of the robots and their target frontier.Through this data, a robot can see which frontiers are unexplored,their position and if any robot has targeted them as its objective


Table 1Characteristics used in classifying the environmental features.

Feature name No. of blobs No. of segments No. of corners Histogram peaks

Corridor 2 2 0 0Corner 2 4 2 0 and 90T -junction 3 5 2 0 and 90Cross 4 8 4 0 and 90Dead-end 1 3 2 0 and 90

Fig. 5. Feature detection, left: corridor, right: branch.

(see Fig. 11). Therefore, the robots will not attempt to explore thesame frontiers. Each robot is aware of the frontier that the otherrobots have aimed to explore, so itwill choose another frontier thatis unexplored and unassigned to any other robot. As a result, therobots will autonomously pick up the tasks in a way that not anyfrontier will be assigned to more than one robot.

The robots repeat this procedure while there is at least oneunexplored frontier in the environment. At the end all odor sourceswill be localized and the whole environment will be explored, nomatter how many odor sources are in the environment or howmany robots participate in the search.

2.2. Environmental features

With the type of environment considered in this paper, thereare five types of features that can be extracted; corridors, cor-ners, crosses, T -junctions (branches) and dead-ends. The robotsrecognize environmental features based on a local metric map(also called local sensing frame) constructed with the range sen-sors while the robot moves across the environment. Fig. 5 showsa robot in different environmental features and Fig. 6 shows lo-cal sensing frames extracted by the robots. The known features areclassified based on the differences that they show in their sensingframes. Similar to several other studies (e.g. [26]) this paper usespattern recognition algorithms that has been addressed in its lit-erature. Blob detection [27], line-segment extraction [28] and cor-ner detection [29] are used to classify the features (See Table 1).A dead-end (shown in Fig. 7) has only one blob that is a uniqueidentification of this feature among all the other ones. T -junctionhas 3 blobs, cross has 4 blobs, corner has 2 and corridor also has2 blobs. To correctly identify the features, similar to the methodused in [30] for corridor detection, the histogram of dominant an-gles of the extracted blobs is calculated to provide more clues tocorrectly identify the features. For example one of the differencesbetween a corridor and a corner is that a corner has a peak in the

Fig. 7. Feature detection process, blob detection and Segment-line extraction in aT -junction (left) and a dead-end (right). The blobs are presented in yellow and theextracted lines are presented in red. (For interpretation of the references to colourin this figure legend, the reader is referred to the web version of this article.)

90° value of its histogram since it has two 90° angles in its segmentlines, but the corridors do not. This histogram is also useful to avoidthe false positive results and is applied to detect the other features(e.g. dead-ends). Table 1 presents the characteristics of differentfeatures based on mentioned parameters. The function IsSimilar()in Algorithm 2 does the blob detection, corner detection, segment-line extraction and histogram calculation on the acquired data andreturns True if the local sensing frame presents the same charac-teristics as the compared known feature.

Algorithm 2: Feature detection algorithm

// Inputs:1// Known_Feature[]: dataset of sensory data of the known features2// M: Number of needed iterations to process an area3// N: Number of needed similar features to recognize that feature as detected4// Output: Detected feature5Start:6i = 17repeat8

Found = False9while !Found do10

S[] = acquire sensors’ data()11foreach Known_Feature[k] do12

if IsSimilar(S[],Known_Feature[k]) then13F[i++] = Known_Feature[k]14Found = True15Break16

until i ≥ N;17MF = Most_repeated_Value_in_F[]18Freq = Frequency_of_MF_in_F[]19if Freq ≤ M then20

Goto Start // did not detect any feature!21return (MF)22

In Algorithm 2 the robot constantly tries to detect the environ-mental features of its surrounding area. If at least in M out of Niterations the robot detects one feature, it considers this feature asthe detected feature of the local area. The values ofM andN depend

Fig. 6. Local sensing frame in different features. Left to right: Corridor, Corner, T -junction, dead-end.


Fig. 8. Artificial attractive/repulsive forces in an unknown environment. d1–d5correspond the distance measured by five sonar sensors. R1–R5 are the artificialrepulsive forces that are calculated based on the inverse distance of d1–d5. ‘‘A1’’represents the artificial attraction to the targeted goal. ‘‘A/R’’ shows the summationof the attractive and repulsive forces and the direction that the robot actually takes.

on (i) the velocity of the robot, (ii) the accuracy of the sensors and(iii) the characteristics of environmental features. If the robot couldnot detectM similar features inN iterations, it must slow-down itsspeed and does the feature extraction of that place from the begin-ning. Based on this method the robots were able to accurately clas-sify the environmental features in the testing environments. It wasfound that this feature extraction and classification process highlydepends on the accuracy of sensory system. The accuracy of thesonars that are used in themaze-like environment tests (explainedin Section 3.1) is less than 3 cm in the range of 1.5 m. Having thisaccuracy, the robots could perfectly distinguish the environmen-tal features from each other. If the environment has more complexfeatures the sensors of the robots also need to bemore complex. Inall of experiments in this paper, it is assumed that the robots areable to accurately classify the environmental features.

This study has tried to design and explain the algorithm in-dependent from feature extraction methods. In fact any kind ofenvironmental feature extraction technique that can lead to topo-logical mapping is appropriate to be used in this project. We usedfive sonar sensors mounted on different sides of robots and Algo-rithm 2 to distinguish basic environment features from each other,i.e. each robot can recognize if it is located in a corridor or in a cor-ner or any other environmental feature. We have also tested thisprocess using a Laser range finder instead of sonars with a fewmi-nor changes in extracting local sensing frames.

2.3. Robots’ motion

The low level of autonomous navigation of a robot relies onthe ability of the robot to simultaneously achieve its target goaland avoid the obstacles in the environment. In an unknownenvironment the robot should reactively avoid the obstacles whileexploring. To avoid the obstacles, a reactive potential field controlmethod [31] was used. The targeted goal of the potential field isprovided by the exploration algorithm (explained in Section 2.1)and the distance to obstacles are measured by the range sensors.In this method, the goal generates an attractive artificial force,while the obstacles generate artificial repulsive forces to therobot. The resulting motion is obtained by the summation ofthese attractive/repulsive forces. Fig. 8 illustrates the action of thismethod in a corridor. Considering the robot holonomic with Nrange sensors, its next velocity v(t + 1t) can be calculated based

on its current velocity v(t) and the forces that are applied to it F(t);

v(t + 1t) = v(t) + µF(t)1t (4)

withµ a constant coefficient. The equation of forces that is appliedto a robot is given by:

F(t) = c1 (Goal_Pose − x(t))n +

N−j=1

c2|d(j)|m

(Vec j). (5)

The position of the robot at time t is described by x(t) ∈ R2.Goal_Pose − x(t) is the distance vector between the robot and itstargeted goal. The first term in (5) is an attraction force towardthe goal that its amplitude is relative to the distance between therobot and its goal. Since d(j) is simply the distance between therobot and the surrounding environment that is reported by thesensor j, the second term is a force that is the inverse functionof the distance of the robot to the surrounding obstacles. Vec j is apredefined vector whose magnitude is set to one and its directionis from sensor j toward the center of the robot. c1 and c2 are twopositive coefficients and n and m are even integer parameters,therefore the first term is always a force toward the goal and thesecond term is a forces from obstacles toward the robot, meaningthat, the robot will try to get far from the obstacles and reach itstargeted goal.

2.4. Path planning improvement for exploration enhancement

Robots’ motion is controlled through potential fields. This tech-nique of motion planning avoids static obstacles very well, but insituations that two or more moving robots are facing to each otherin narrow corridor, this techniquemakes themovements very slowand the performance of the mission gets low. In order to improvethe exploration process, this paper presents some control rules toprevent this kind of situations. These rules are shown in Algorithm3. In this algorithm, each robot considers a virtual bubble arounditself. Once another robot enters to this bubble, this robot checksthe probability of direct collision between them. The policy is togive priority to one of them, so one of them stops and the otherpasses through the area. If one of the robots is located in the al-ready explored area, the one that is exploring a frontier will havehigher priority. But if both are in the same conditions the one thathas lower robot ID has the priority (Algorithm 3).

2.5. Map merging

Consider several robots searching in a single environmentwhile each one has its own coordinate system. Their axes ofX and Y do not match with each other and moreover they donot know where the reference point of the other’s localizationsystem is. Each one of them is generating their own topologicalmap of its visited local area. They are simultaneously sendingthese local self-generated topological maps to each other. Theproblem is ‘‘how each one of them can integrate the data comingfrom the others to its local map and generate a bigger map?’’.Fig. 9 shows an example of this problem. There is no centralstation unit for attuning the robots; moreover, there is no specificlandmark in the environment. Therefore, the robots should solvethe problem in a distributedway. Similar to [32,20], in thismethod,the generated map represents more than just the structure ofthe environment. Additional information, such as the degree ofvertices, the orientation of edges at vertices, and other attributes,is recorded and stored in annotations of the graph. Fig. 11 showsan example of the topological map’s data with some descriptions.The next step is to match the topological maps and generate a


Algorithm 3: Path planning improvement

Calculate a confined circular area around the robot()1if any other robot is inside the circle then2

if there is a possibility of being in a direct collision pattern then3if it is a direct collision path then4

Recalculate the objective()5

else6if both currently exploring frontiers OR both moving inside7explored area then

//Give priority to the one which has lowest ID.8if the robot ID is higher that the other robot’s ID then9

go back to previous node and stop for a while() // so10the other robot passes

else11Continue navigating12

else13//Give priority to the one that is exploring a frontier.14if the robot is inside explored area then15

go back to previous node and stop for a while() // so16the other robot passes

else17Continue navigating18

else19Continue search and exploration algorithm.20

Fig. 9. An environment being explored by two robots with different coordinatesystems.

merged global map out of them.We consider the problem of ‘‘mapmerging’’ as a ‘‘graph matching’’ problem (see Figs. 9 and 10).One of the problems in graph matching is error-tolerant subgraphisomorphism. The robots should identify a common subgraphwhen there may be missing vertices and edges.

The proposed method is a decentralized frontier based algo-rithm enhanced by a cost/utility evaluation function that considersthe odor concentration and airflow at each frontier.

Common subgraphs H1 = [V , E, k] (where V is the set ofvertices vi, E is the set of edges ei and k is the subgraph size) andH2 = [W , F , k] of two given graphs G1 and G2 are isomorphic toeach other if there is such numeration of subgraphs’ vertices x(i)and y(i), that ∀i, j ∈ {1 . . . k}

Equivalence_vertex_function(vx(i), wy(i))?= true (6)

Equivalence_edge_function(e(x(i),x(j)), f((y(i),y(j))))?= true. (7)

This paper defines the stated terms as following:

‘‘Equivalence_vertex_function’’: Two vertices are equivalent iftheir vertex types (presenting environmental features) match

Fig. 10. Matching generated maps of two robots exploring the environment inFig. 9.

with each other. Sincewe only have five types of environmentalfeatures in structured environments (corridor, corner, T -junction, cross and dead-end), this literally means that a vertexthat is presenting a cross is not matched with a corridor or aT -junction.‘‘Equivalence_edge_function’’: Two edges are equivalent if theirlengths are approximately equal and their connecting verticesare equivalent. Now we just look for numbered sets X =

{x(i)}ki=1 and Y = {y(i)}ki=1, satisfying conditions (6) and (7).In other words, it is required to find all pairs of matchingvertices in the subgraphs which are connected by matchingedges (including the null edges). Since the coordination systemsof the robots are not aligned, the conditions (6) and (7) arenot defined based on the position of the vertices (despite manyprevious works in this field). By that, the subgraphs match witheach other regardless to the positions of the vertices but basedon their topology. In the current work it is assumed that therobots start from the same point (usually an entrance of thebuilding) at the beginning of the mission. By this assumptionit is guaranteed that the first vertex of all local maps is fromthe same point, therefore the graphs always have at least onecommon subgraph and the robots are able to merge their mapsin any case, while their coordinate system is notmatched to oneanother.

Whenever a robot finds a new feature in the environment, itadds this feature as a new node to its local map and sends amessage to all the other robots and reports the new map. If arobot is walking inside already explored environment and getsinto another feature that is different from its previously detectedfeature, it modifies this feature in its local map and again sendsa message to all the other robots and reports the modifies map.On the other hand, each robot has a running memory-residentprogram that always is listening to the network and receives allthe messages that are sent by the other robots. When a robotreceives amessage that shows another robot has found ormodifieda feature in its map, it starts the hypothesis building process bycreating the list of all vertices in the local topological map that arestructurally compatible with the newmap (conditions (6) and (7)).Vertices are tested for compatibility by examining their attributes:exactly known attributes (e.g., vertex type) must match perfectly;inexactly known attributes (i.e., due to measurement error) mustbe compared with a similarity test.

For the similarity check of inexact attributes, a threshold isdefined based on the accuracy of sensors and the localization


Fig. 11. An example of topological map data.

system. Since the localization is improved using the methodexplained in 2.6 the measurement error in the environmentalfeatures was always less than a few centimeters. Therefore athreshold of 10 cm is defined to compare the feature nodes oftopological maps. This means that if one corridor is 90 cm length,it is similar to a corridor that is 99 cm. However, it is practicallyimpossible that the robots make this mistake since not only thefeature type and other attributes (e.g. length and width) must besimilar for two features, but also the nodes that are connectedto them should be the same. The threshold of this similarity testraises a constraint of minimum feature size and minimum featuredistance to the testing environments. Having a threshold of T insimilarity checkmeans that theminimum length of environmentalfeatures must be bigger than T and the distance of two similarenvironmental features (features of the same type, e.g. the distanceof two corridors) should be at least T . In these conditions therobots correctly distinguish similar environmental features fromeach other.

Once a robot finds a subgraph in anothermap that is isomorphicto a subgraph of its own map, it will merge these maps togetherafter finding the geometric transform function that converts thepositions of the vertices of the secondmap to its currentmap. Sincea position correctionmethod is used (described in the next section)that corrects the localization of the robots, a linear function isgood enough to transfer the coordinates of two maps to eachother. The transform function is defined by a linear geometriccalculation. Finally, the robot is able to add all the nodes of theothermap to its ownmap after transforming their positioning datato its coordinate system. Having this transform function, the robotsare also able to know where exactly the other robots are locatedand which frontiers are assigned to which robots. If the robotsare not in communication range ∆, they independently explorethe environment and share the maps whenever they are able tocommunicate.

2.6. Localization correction

Robots’ localization is a key issue in multi-robot mapping andexploration. This work does not consider a global positioningsystem. The only tool that the robots have for determining theirposition is their odometry. However, the odometry is unreliablebecause of uneven floors and wheel slippage. It is therefore

necessary to increase localization accuracy by measuring positionof the robot relative to known objects in the environment.

Normally odometry errors accumulate incrementally as therobot is traveling, therefore, the robot’s localization is moreaccurate at the beginning of an experiment and loses its accuracyduring the test. If the robot enters in an environmental featurethat has already been explored, it can look at the map and findthe start position of that feature, then correct its localization basedon the data that has been stored in the map. It does not matterif this feature was added to the map by the current robot or byanother robot in the team, since the feature has been added tothe map in the past; it means that the location that was savedin the map is more reliable than the current localization of therobot. This method is only used when the robot is passing an areathat has already been explored. In addition to correcting odometryerrors usingmap’s data, the robots are able to correct their internalodometry angle based on the features of the environment. Sincein usual structured buildings, all the corridors are parallel orperpendicular to themain direction, similar to [33],when the robotdetects a corridor and ismoving along it, the robot is able to correctthe localization angle. This method is used in the unknown area aswell as in the already explored environment.

3. Experimental results3

The proposed method has been tested and validated both inreal world and in simulation. The functionality of the method insearching and exploration in a structured real environment hasbeen verified. Afterward, in order to evaluate the method in morecomplex environments, the method has been tested in simulation.

3.1. Experiments in maze-like environments

The proposed method was tested in different reduced scalemaze-like environments, like the one shown in Fig. 14. This testingarena,with 3×4m2 area by 0.5mheight, has controlled ventilationthrough a manifold that extracts air from the testing environmentthrough a honeycomb mesh integrated into one of the walls.The opposite surface of the environment contains a similar meshthat allows the entrance of clean air that flows through theenvironment. Controlled gas sources are simulated with ethanolvapor, generated using bubblers and pumped to different placesof the environment through a set of PVC tubes. Fig. 13 shows achemical release mechanism that is used as odor source in theexperiments. Inside this environment, amaze like structure is builtto simulate real structured buildings. This environment is consistof corridors, crosses, corners and dead-ends.

3.1.1. The robotsThe iRobot4 Roomba was used in the experimental tests.

This is an attractive platform because it is inexpensive, readilyavailable and can be fully monitored and commanded through aserial port interface. In the current work, a set of Roombas wereupgradedwith a small laptop computer (ASUS Eee PC 901) runninga Linux based operating system and the Player5 environment.The computer interfaces through a micro-controller board witha set of five sonars, three 2-D anemometers and a gas sensingboard (Fig. 12). Based on values measured by sonar sensors, therobots recognize the basic environmental features (e.g. corridors,

3 The source code of all parts of this study are available online inhttp://www.isr.uc.pt/~ali/multiexp.htm.4 http://www.irobot.com.5 http://playerstage.sourceforge.net.

http://www.isr.uc.pt/~ali/multiexp.htm

http://www.irobot.com

http://playerstage.sourceforge.net


Fig. 12. iRobot Roomba robot equipped with a laptop and sonar, gas, andanemometer sensors.

Fig. 13. The chemical release mechanism.

dead-ends, etc.). The gas concentration was measured with acustom sensing board based on metal oxide gas sensors (Figaro6

TGS2620). The directional anemometer was designed based onthree self heated NTC7 sensors placed around a triangular prismand processing the raw measurements with a method similarto the one previously described in order to estimate the winddirection [34]. The Player/Stage driver for Roomba robots makesit possible to run the same code either in simulation or on the realRoomba robots.

3.1.2. Experimental validationIn order to evaluate the method, the algorithm was experi-

mented in the previously described environment with one, twoand three Roomba robots separately, first without having any odorsource, and then with an odor source releasing gas in the left-bottom corner of the arena at eight centimeters height, as shown inFig. 15(1). The robots have no a-priori knowledge about the num-ber of odor sources existing in the environment. When there is noodor source in the environment, all frontiers have the same utilityand the search algorithm acts like a pure frontier based explo-ration algorithm that is enhanced by avoiding collisions betweenthe robots. The method is validated by comparing the results andanalyzing the behavior of the robots.

Fig. 14 shows three robots exploring a small maze whilesearching for odor sources. In this experiment there were no odorsources. All robots started from the same point but not at the sametime and were run a few seconds after each other (like if they

6 http://www.figarosensor.com.7 Negative Temperature Coefficient.

Fig. 14. Three robots exploring a gas free environment.

were deployed at the entrance of a warehouse). The red footprintshows the first robot’s path, the blue footprint is related to thesecond robot and the green shows the footprint of the third robot.These experiments show the functionality of algorithm in differentmaze structures and with different number of robots. Frame 3 of

http://www.figarosensor.com


Fig. 15. Three robots exploring the environment and finding the odor sources.

Fig. 14 shows an example of coordination between the robots;when the second robot reached the junction it figured out thatthe path in the front was already explored so it chose the leftpath. In frame 5, the third robot had decide to either take thefront or the left path, since both paths were already explored, it

Fig. 16. Generated maps for the environment in Fig. 4, red: without localizationcorrection, green: with localization correction. (For interpretation of the referencesto colour in this figure legend, the reader is referred to the web version of thisarticle.)

Fig. 17. Exploration time, with/without odor source.

checked the nearest unexplored and unassigned frontier in themap that was provided by the other robots and finally chose thefront path. ‘‘Path planning improvement’’ is presented in frames 9and 10, when the second robot goes backward for a while to avoidpossible collisions with the first robot. Fig. 16 shows a topologicalmap generated by the robots in one experiment over the realtopology of the testing environment. The red lines and verticesdemonstrate the generated topological map before implementingthe proposed localization correction method, and the green showsa map after applying the localization correction technique. Theeffect of localization correction technique is significant, e.g. all thelines of the green map are parallel to the corridors. The robotscorrect their odometry during the mission so the maps’ errors donot accumulatively increase. The maps out of several tests weresimilar, however they did not match perfectly.

Fig. 15 shows the tests done with the same maze structure andthe same robots, but with an ethanol odor source on the left sideof the environment. The first robot in the first branch decided togo to the left-way because it sensed an odor clue in that direction(red footprints). The other robots also automatically changed theirpath in accordance with the new conditions. The results showthe effect of odor concentration on the behavior of the robots. Ineach test the exploration time, that is the time it takes to coverall the maze paths, is measured. Fig. 17 shows that the completeexploration time is higher when considering gas cues, howeverthe difference is less than 21%. Fig. 18 shows the time to reachthe target (the location of the odor source) in these two scenarios.The chart shows that the robots reach the target much faster withhaving gas cues rather than without having it, which shows thefunctionality of the algorithm. Each result is the average of fivesimilar tests. Different tests with constant conditions had similarresults with about eight percent variance. The maximum speed of


300

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Fig. 18. Reaching the target (the location of the odor source), with/withoutolfactory cues. Without odor source the method acts like a pure frontier basedexploration algorithm.

Fig. 19. Model of a testing environment in simulations.

the robots was kept constant in the tests. More experiments weredone by placing the odor source in other locations of the arena andthe results were similar to the graph in Fig. 18.

The robots were initially released at the downwind side of thetestbed (similar to many other previous works namely [10,8]) inthe experiments. This is the same task that the handlers of SaRdogs do at the start of the search mission. In fact if we positionthe robots on the other side of the environment (upwind side)they will not be able to sense the odor and therefore they willexplore the environment until they eventually get into the activeodor plume area. The same point is valid if there is no air-flowin the environment, i.e. in this case the robots mostly explore theenvironment rather than tracking odor plumes and anyways theywill intend to explore the frontiers with higher odor concentrationfirst.

3.2. Performance assessment

For evaluating the exploration algorithm and measuring itsperformance, the Player/Stage framework [35] was used, since inthe real world, there are several constraints that do not alloweasily testing the proposed method. The Player/Stage frameworkprovides a very easy way of testing and monitoring the proposedalgorithm in different configurations.Most of the parts of proposedmethod including decision-making, feature extraction, robots’motion, localization correction, path planning improvement, andmap merging are imported to the simulation with a few minormodifications. Only some parts related to sensory interfaces aretotally changed in the simulations. Regarding the world model, incompare with real-world experiments, bigger and more complexenvironments are tested in the simulations.

PlumeSim [36] is used for simulating odor plumes insidePlayer/Stage. It is capable of feeding simulated chemical plumesinto Player/Stage from a wide variety of sources, from analyticalmodels to data generated by CFD software.

In this work themodel of testing environment that is presentedin Fig. 19 was given to ANSYS Fluent CFD [37] software to simulateodor sources and provide odor concentration data. 3-D ANSYSFluent is used to simulate the odor plume, however, the robotsmove on the 2-D floorwith gas sensors at 10 cmheight, so, only theairflow and concentration measured by the robots at that heightare relevant for their decisions. Fig. 20 shows four snapshots of 3-Dplumes propagation during the time in one of the tested scenarios.The 2-D data is given to the PlumeSim simulator in Player/Stageand is shown in Fig. 21. This environment contains three simulatedodor sources in three corners of the arena (the other corner is theentrance door). The airflowwas ventilated from the inlet side (left)with constant speed of 0.5 m/s. Since the environment containsseveral obstacles, the flow velocity varies in different parts of theenvironment. For example in the left-top corridor, the air has lowervelocity relatively to the bottom corridor since there is an obstaclein its front. As it is shown in the simulation snapshots of Fig. 20, theodor propagation directly depends on the airflow velocity.

Fig. 22 shows the average results of five tests with one, two andthree robots. A conclusion from Fig. 22 is that havingmore robots ismore advantageous in finding more odor sources. Similar to manymulti-agent systems, reduction of operation time by using morerobots is calculated by speed up formula as below:

Speedup =TsingleTmulti

Fig. 20. ANSYS Fluent simulation results; contours of mass fraction of ethanol propagated in the testing environment of Fig. 19 during the time.


Fig. 21. Robots searching for simulated odor sources in Player/Stage.

600

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Fig. 22. Results for different number of robots finding multiple sources.

Spee

dup,

se

arch

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odor

sou

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1.5

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Fig. 23. Speedup for different number of robots finding multiple sources.

where:

• Tsingle: Operation time of the method using one robot.• Tmulti: Operation time of the method using several robots.

In Fig. 23, it can be seen that speedup ismore formore odor sources,i.e. having more robots in the operation will result in better searchtime in looking for more odor sources.

The worst case in the search operations is when the robotssearch for odor sources but there is no odor source in the envi-ronment. In this case the robots have to cooperatively explore thewhole environment without having any olfactory cue. Therefore,themethod is tested in simulation in different environments with-out having any odor source to evaluate its ‘‘exploration’’ ability incomplex scenarios.

One of the possible ways to measure the performance ofthe proposed method is to compare it with an optimal method.However, there is no optimal method for exploring an unknownworld. If the robots had prior map of the environment, the

Fig. 24. A maze with 34 nodes.

exploration problem would be reduced to the standard well-known ‘‘multi-agent traveling salesman’’ (TSP) problem. It is veryobvious that the exploration method will never be better thansolution of traveling salesman problem, because the robots do nothave a priorimapof the environment before exploring but TSP doeshave the world model before the mission. However, the proposedsearch and exploration method are compared with the results oftraveling salesman solution as an unreachable, more than perfect,optimal method. This can be a good criterion for evaluating themethod.

Themethodwas testedwith different number of robots in threedifferent mazes (see Figs. 24–26). These mazes were also used torun an optimal TSP algorithm and the results are compared inFig. 28. The graphs show the average of five tests for each data.The variance was less than five percent. The results show that theexploration time improves with higher number of robots. Anotherconclusion is that having more robots is more advantageous in acomplex maze than in a simple maze. This also shows that thecooperation algorithm in this approach is efficiently functional.

A repeated node is a node that robots pass more than once.Another good parameter for measuring the performance of themethod is counting repeated nodes since these represent lossesof performance. Fig. 27 shows the number of nodes that havebeen repeated more than once in the TSP algorithm as well asin the proposed method for the maze shown in Fig. 24. AlthoughFig. 27 shows that the number of repeated nodes is more in theproposed method comparing with TSP, the results are acceptablycomparable especially considering the fact that TSP has priorknowledge about theworld but the robots in the proposedmethoddo not.

A conclusion from Figs. 27 and 28 is that there is a trade-offbetween the number of robots and the size of the world since thenumber of repeated nodes tends to increase with the number ofrobots (e.g. when the number of robots is four, for themaze shownin Fig. 24, the number of revisited nodes is more than when thereare three robots). However, Fig. 28 shows that even in this case theexploration time is improved.

The algorithms and their parameters were modified and ad-justed after several simulation experiments. For example, the valueof k (common subgraph size) in conditions (6) and (7) is a criticalissue. Based on simulation experiments, it was figured out that itis good enough to set k to eight. This means that if two robots finda common subgraph with eight vertices, they can merge their lo-cal maps together. However, this value is highly dependent on thestructure of the environments. If an environment is rich of unique




features this value should be less and if an environment is full ofsimilar features this value must be higher.

The effectiveness of the algorithm was investigated as afunction of the number of robots available. Ideally, it would beexpected that doubling the number of agents would halve the timerequired for search and exploration. However, Even with a priorimap information it is often not possible to achieve this amount

Num

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epea

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Fig. 27. Number of repeated nodes, comparing the results of the proposedmethodwith the TSP method in maze shown in Fig. 24.

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in)

35

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Fig. 28. Test of various numbers of robots against complexity of the environment,1: maze in Fig. 24, 2: maze in Fig. 25, 3: maze in Fig. 26.

of performance increase. The ideal number of agents for the mostefficient exploration of an environment depends on the nature ofthe environment. The large environment of Fig. 26 showed greaterimprovements in the exploration performance when the numberof agents was increased. Another significant point is that themethod is fully functional even in the case of some robots’ failures.If one or several robots fail during the operation the other robotswill accomplish the mission. A not studied problem is when thereare partially malfunctioning robots, in this case the robots mightsend wrong information about the environment to the others andthe whole mission may fail.

Regarding olfactory search the presentedmethod does not haveany assumption regarding air flow. If there is detectable air flowin the environment the robots take this clue inside their decisionmakings, but if there is no air flow the search algorithm acts likea cooperative exploration method. In the later case, the robots caneventually find odor sources (if there is any) during the explorationof the environment.

4. Conclusions and discussions

An algorithm for multi-robot odor source search, explorationand topological mapping inside large buildings composed by par-allel and perpendicular corridors (warehouse-like environments)was proposed. The algorithm was experimentally validated ina small, realistic testing environment with up to three Roombarobots. Details about all the complementary aspects required tosuch implementation are provided. These include motion control,feature extraction and classification, localization andmapmerging.The exploration efficiency is improved by integrating odor sens-ing cues in the frontiers selection so they navigate toward the odorsources and localize them while cooperating with each other bysharing information in their local maps. If the robots do not senseany olfactory clue, they try to efficiently explore the environment


until they get into active odor plume area. When a robot is insidean odor plume (sensing high odor concentration) it intends to trackthe plume by traveling in the upwind direction. In the case thata robot is in a situation that wants to make decision to select afrontier, it picks up the frontier with highest odor concentration. Interms ofmapping, the robots generate the topologicalmap of envi-ronment during exploration. Map sharing is themain tool for auto-matic distributed task sharing and cooperation in this method. Therobots merge their topological maps based on common subgraphisomorphism techniques even if they do not have common coor-dinate systems. Based on the stored information inside the maps,each robot knows the location of unexplored frontiers and assignsone of them to itself as a target to explore, so the other robots willnot select the same frontier.

In addition to real world experiments, the algorithm hasbeen simulated against a large variety of configurations in thePlayer/Stage framework. Odor plumes have been simulated usingPlumeSim in Player/Stage. The effect of the number of robots onexploration in different type of environments has been analyzedand discussed. The results of real world experiments show theeffect of gas cues on the behavior of robots and it shows that usingthe proposed algorithm, robots firstly explore the areaswith higherprobability of existence of odor sources. Simulation results showthat having more robots is more advantageous in a complex mazethan in a simple maze.

Finally, there are some issues that should be discussed here.Although, most of the real world human-made structures canbe modeled to the kind of environments that are tested in thispaper but the question is if this approach can be easily adaptedto a situation in which the robot cannot be assumed to be ableto reliably perceive the feature it is traveling through. In morecomplex environments, as it was previously stated, the featureextraction method that is used in this study can be replacedby another methodology providing better results in the targetenvironment. Even if the range of the sensors is limited or thereis noise in sensing, the robot can perform some movements inthe environment to correctly identify the environmental features.The number of defined features can also be increased in suchcomplex environments. However, if the robots are not able tocorrectly identify the environmental features, the topologicalmapswill have some degree of unreliability and map merging cannotbe done based on similarity checks of this paper. In most ofthe human constructed facilities, the robots are able to classifythe general features of the environment (maybe by using morecomplex sensors including 3D Laser range finders and cameras)and so this method can be applied.

Regarding possible conflicts between the robots in their task-allocations, three points should bementioned. First, the topologicalmaps are very small data files that are transferred in a fewmilliseconds and there is not a big delay in the communicationsbetween the robots. Second, it must be very rare that two robotswant to choose frontiers exactly at the same time because most ofthe times the robots are traveling without changing their frontiers.Third, even if two robots are going to choose frontiers at the sametime, they might automatically choose different frontiers basedon their positions and the utility/cost function. Considering thesethree points, the probability of two robots to choose the samefrontier is very low. Even if this happens (that we have never seenin the experiments and simulations) two robots will temporarilytravel to explore one frontier, however, as soon as one of themgets to a new detected feature, it will process the map to pick up afrontier and for sure this time it will not choose the same frontiersince it is already taken by the other robot. The other point is thatin the Algorithm 1 the robots make their decisions independentlyand there is no condition that may lead to a deadlock between therobots. In Algorithm2 that is for improving the path planning of the

robots, there are priority checks between the robots. The robot thathas lower priority should go back or wait for some seconds untilthe other robot passes. If The other robot fails at this moment, thisrobot will be waiting forever. This issue can be avoided by addinga timeout threshold in the Algorithm 2.

Acknowledgments

The authors would like to thank the anonymous reviewersfor their insightful and constructive comments that helped inimproving the manuscript. The authors also would like to thankJoão Nunes and Gonçalo Cabrita for their contributions duringimplementation of the method. This work was partially supportedby the European project GUARDIANS contract FP6-IST-045269 aswell as by the Portuguese Foundation for Science and Technologycontract SFRH /BD /45740 /2008.

Appendix. Supplementary data

Supplementary material related to this article can be foundonline at doi:10.1016/j.robot.2011.07.010.

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Ali Marjovi received his B.Eng. degree in ComputerEngineering from University of Isfahan, Iran in 2003and his M.Sc. degree in computer-hardware engineeringfrom Sharif University of Technology, Tehran, Iran, in2005. He is currently a Ph.D. candidate in Institute ofSystems and Robotics, University of Coimbra, Portugal.His research interests includemulti-robot olfactory searchand exploration, and swarm robotics olfaction.

Lino Marques has a Ph.D. degree in Electrical Engineeringfrom the University of Coimbra, Portugal. He is currentlyan auxiliary professor at the Department of Electricaland Computer Engineering, University of Coimbra, and heheads the Embedded Systems Laboratory of the Institutefor Systems and Robotics from that university (ISR-UC).His main research interests include embedded systems,mechatronics, robotics for risky environments, artificialolfaction systems and mobile robot olfaction.

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Multi-robot olfactory search in structured environments