Boeykens, Jens

Boullart, Luc

Wyns, Bart

English

Ghent University Academic Bibliography

MAP EXPLORATION USING A LINE-BASED FORMATION OFMOBILE ROBOTSBart Wyns, Jens Boeykens, Luc BoullartDept. of Electrical Energy, Systems and Automation, Ghent University, Technologiepark 913, Zwijnaarde, BelgiumBart.Wyns@UGent.beKeywords: Map exploration, formation strategies, mobile robots, concave obstaclesAbstract: Exploration of an unknown environment is a well-studied problem for single robot systems. However, usingjust a single robot limits the speed in which a map can be fully explored. Using a multi-robot approach,a noticeable performance gain can be achieved. In this article a line-based formation strategy to explore astatic area is introduced, without making any assumptions about the shape of the obstacles within. A softwaresimulator including this line-based formation strategy was built to evaluate performance in different environ-ments. Results show that the robot formation can easily get around both convex and concave obstacles whilstconstructing a map that is both complete and correct.1 INTRODUCTIONMap exploration is one of the most widespread tasksperformed by mobile robots, due to many practicalapplications. Mine field clearance, planetary explo-ration and rescue missions are just three out of manyexamples. It is therefore considered as one of the fun-damental problems in mobile robotics. Since the po-tential of a single robot is quite restricted, an increas-ing amount of research is focused on multi-robot ex-ploration (A. U. Irturk, 2006; K. Easton and J. Bur-dick, 2005; W. Burgard et al., 2005; W. Kerr et al.,2005; S. Weihua et al., 2006; Simmons et al., 2000).An increased performance, distributed sensing andhigher fault tolerance are evident motives. However,moving from one robot towards a multi-robot setupalso strongly increases complexity.Exploring unknown territory can be categorized incomplete coverage (I. Rekleitis et al., 2004) and nocomplete coverage (as in (Rogge and Aeyels, 2007)for example). The latter use a zigzag formation withtwo leader robots. Whenever an obstacle is found, theformation beaks up in two groups, always between themiddle robots. The major drawback of their strategyis the lack of support for concave obstacles. Further-more recursive splitting is bounded by the number ofavailable robots, which in turn limits the density ofthe obstacles spread across the area. Coordinating therobots with a formation obviously implies establish-ing and maintaining that formation. In (J. Fredslundand M. J. Mataric, 2002) this is achieved with localsensing and minimal communication. Multiple robotsalmost always need to communicate in order to avoidcollisions and minimize repeat coverage. It is oftenassumed that communication is limited (W. Burgardet al., 2005), sometimes with the restriction of line-of-sight (I. Rekleitis et al., 2004; J. Ota, 2006). In thisarticle we shall not adopt this assumption, consideringit as future work. If communication is indeed limited,a message can always be passed on from one robottowards another until its destination is reached, justas if the robots were forming a chain. A more com-plete and general overview of exploration and cover-age strategies with multiple robots is found in (A. U.Irturk, 2006; H. Choset, 2001).The main goal of this article is to design a strategywhich guarantees correct exploration with multiplerobots, resulting in a complete map of the area. Weonly require free space to be covered by the robot’ssensors, not by the robot itself. A formation is used tocoordinate the robots which explore a room one sliceat a time. Furthermore we don’t make any assump-tions about the shape of the obstacles in the environ-ment, unlike many related work. Both concave andconvex obstacles are thus allowed. At the same timewe try to obtain maximum benefit from the number ofrobots that are available for exploration.The remainder of this paper is organized as fol-lows. Firstly, we describe the exploration strategy fora general type of robot using a theoretical approachin section 2. More details about the implementationwithin the simulator can be found in section 3. Fi-nally, we discuss the results in section 4, followed bysome conclusions in section 5.2 ALGORITHM DESCRIPTIONIn our approach, robots are ordered on a straight line.An important advantage of a straight line is its width,which is the largest of all possible formations. In thisway a large part of the map can be explored at once. Inaddition it’s a very simple formation, which allows foreasy establishment via a chain of friendships (J. Fred-slund and M. J. Mataric, 2002). As for the explorationitself we use a complete sensor coverage approach asin (Rogge and Aeyels, 2007).Like many previous work, the robots explore theenvironment slice per slice. The deliberative ap-proach differs a lot from traditional algorithms (suchas behavior-based systems) as we don’t simply splitthe robot team in two groups, each wall-following adifferent side of the obstacles. Instead, two separatephases are executed when obstacles are found. If onerobots detects an obstacle, this is communicated tothe team and all robots stop. Next, they start the firstphase in which all new obstacles are added to the map(fully explored via wall-following). This is called theexploration phase and is illustrated in figure 1. Whenno new obstacles can be found this phase is termi-nated and the robots initiate the second phase. In thisphase the robots move round the obstacles until eachrobot has passed it and the formation is regrouped. Ifduring the second phase a new obstacle is found or anobstacle is preventing the formation to regroup, therobots go back to the exploration phase. This processcontinues until the formation is reestablished and noobstacles are blocking their path. The formation canthen explore the remainder of the slice.Once no new obstacles can be found near therobots, they start moving round each obstacle block-ing their path. Since each of those obstacles is alreadyadded to the map, they can locate their correct goalposition. Each goal position is chosen so that all ar-eas are explored, in particular the cavities of concaveobstacles. To do so an imaginary line is drawn, start-ing from the robot’s position and going through theobstacle blocking its path. As soon as enough freespace is found, a goal point is located.3 EXPERIMENTAL SETUPWe implemented our strategy on the EyeBot-platformwithin a simulator called EyeSim (A. Koestler and T.Bra¨unl, 2004). In addition we built a generic test suiteproviding easy testing, configuration and evaluation.The setup comprises several robots and one computeracting as a server. The robots are fully autonomousand can communicate both directly with each otherand with the server (wireless).3.1 EyeBotServerThe server has several tasks. Firstly, it is responsi-ble for the configuration of each experiment. It readsin a configuration-file containing parameters like theshape of the formation, driving speed, width of slices,etc. These parameters are sent to all robots whichmeans configuration is centralized and the need to re-compile the robots program is eliminated. It also al-lows for easy extending the suite with other forma-tions and strategies resulting in a very generic plat-form. The second task of the server originates fromthe exploration of an environment. It is responsiblefor storing the global map of the area, constructedby the robots. We do this to adjust for the limitedmemory capacity of the robots and to provide a saferstorage of the map. Since a computer has a largeamount of memory, we use a grid to model the envi-ronment. A grid makes it easy to calculate goal pointsand the shortest route towards them. The last task ofthe server comprises exporting the results of the ex-ploration. A visualization of the map via a PNG im-age is generated from the grid structure and stored ondisk.3.2 EyeXploreEyeXplore is the main program run by each EyeBotand is an implementation of the deliberative strat-egy described in section 2. We emphasize that therobots running this program are fully autonomous inthe sense that they don’t get instructions from theserver. For them the server acts as an extended mem-ory in which the map can be stored. During explo-ration, the robots are responsible for determining theedges of obstacles which are then send to the server.The robots don’t store these edges permanently in lo-cal memory in order to save space. There is thus nodistributed map among the robots. In order to explore(a) Exploring robots in front of different obstacles.(b) In case of larger obstacles, the two exploring robots might explore the same obstacle.When they actually meet each other during wall-following, they know there is only oneobstacle. Both robots then only explore half of the obstacle’s border to gain speed.Figure 1: Illustration of exploration phase. Two explorer robots are selected and perform a wall-following(a) (b) (c)Figure 2: Robot collisions during wall-following to reach goal-pointFigure 3: PSD sensors used during the experiments.the edges of obstacles and to avoid collisions, we usedistance sensors as shown in figure 3.4 RESULTSIn this section we discuss some results of the delib-erative strategy. We examine the exploration time forfive different environments (shown in figure 4). Dueto page restrictions only a single map is shown.In order to examine the influence of the numberof robots on the exploration time, each environmentwas explored five times. For each test the number ofrobots was increased with one, starting from a sin-gle robot until a maximum of five robots. This givesus five durations in total for each environment (thirdcolumn of figure 4). From the results it is clear thatintroducing a second robot always leads to a faster ex-ploration with respect to a single robot. This is causedby a higher parallelization and a bisection of the num-ber of slices required to explore the area. Bringingin even more robots doesn’t always have the samepositive effect. This observation has several causes.Firstly, remark that the extra decrease in the numberof required slices declines as the number of robots in-creases. Another cause is that within our strategy, atmost two robots are exploring new obstacles at thesame time. The other robots are stalled during thattime, which can lead to relatively long delays depend-ing on the obstacles within the environment. In figure4 the large S-shaped obstacle is explored by all robotswhen the formation contains only two robots. Addinga third robot causes this obstacle to be explored byjust one robot, introducing delays for the remainingtwo robots. This explains the large increase of explo-ration time. Also note that the obstacles in figure 4 areplaced such that the formation cannot be maintainedas soon as there are more than two robots, since thereis not enough space to form a line. This also meansthe robots will hamper each other more often whichhas a negative effect on exploration time.We also examined the influence of the width of theformation and thus the width of the slices. For thisFigure 4: Results for a difficult map (4 other maps are not shown due to page restrictions). Four columns are shown for eachenvironment. From left to right: the simulator, the resulting PNG image of the map, exploration time with increasing numberof robots and finally with an increasing slice width.purpose each environment was explored five timesonce more. Now each test is done by a forma-tion comprising a constant number of robots, namelythree. Instead, we vary the horizontal distance be-tween each two neighboring robots standing in a lineformation. The smallest distance is 33 cm and thisis increased with 10 cm in each step, correspondingto the width of a single EyeBot. The largest distancebetween two robots considered here is thus 73 cm.The exploration times for each environment areshown in the fourth column in figure 4. Note thatthe graphs now show a more clear descending ten-dency and exploration times are lower than when weincreased the number of robots. This is caused bythe decreasing number of slices as we increase slicewidth. Fewer delays are introduced as well becausefor most of the test-environments two robots were ex-ploring at the same time, implying more paralleliza-tion. The robots also don’t hinder each other as oftenas with a formation comprising four or five robots.Therefore we can assume it is better to first increaseslice width in order to lower exploration times. Ofcourse the range of the sensors and communicationhardware will limit the maximum width and eventu-ally additional robots should be used as well.5 CONCLUSIONS AND FUTUREWORKWe developed a deliberative strategy to explore an un-known environment with a formation of robots. Bothconcave and convex obstacles are supported, which isthe primary innovation of our research. Moreover, thestrategy is very scalable as it can be applied from asingle robot to any number of robots (as long thereis enough space in the environment). The resultsfrom our tests are very promising, yet further researchis still needed to exploit the number of robots evenmore.REFERENCESA. Koestler and T. Bra¨unl (2004). Mobile Robot Simulationwith Realistic Error Models. International Conferenceon Autonomous Robots and Agents, 6:46–51.A. U. Irturk (2006). Distributed Multi-robot CoordinationFor Area Exploration and Mapping.H. Choset (2001). Coverage for robotics - A survey of re-cent results. Annals of mathematics and artificial in-telligence, 31(1-4):113–126.I. Rekleitis, V. Lee-Shue, Ai Peng New, and H. Choset(2004). Limited communication, multi-robot teambased coverage. Proc. 2004 IEEE International Con-ference on Robotics and Automation, 1-5:3462–3468.J. Fredslund and M. J. Mataric (2002). A General Algorithmfor Robot Formations Using Local Sensing and Min-imal Communication. IEEE transactions on roboticsand automation, 18(5):837–846.J. Ota (2006). Multi-agent robot systems as distributed au-tonomous systems. Advanced engineering informat-ics, 20(1):59–70.K. Easton and J. Burdick (2005). A coverage algorithm formulti-robot boundary inspection. IEEE InternationalConference on Robotics and Automation (ICRA), 1-4:727–734.Rogge, J. and Aeyels, D. (2007). A novel strategy for explo-ration with multiple robots. Icinco 2007: proceedingsof the fourth international conference on informaticsin control, automation and robotics, vol ra-1- roboticsand automation, 1:76–83.S. Weihua, Y. Qingyan, T. Jindong, and X. Ning(2006). Distributed multi-robot coordination in areaexploration. Robotics and autonomous systems,54(12):945–955.Simmons, R., Apfelbaum, D., Burgard, W., Fox, D., Moors,M., Thrun, S., and Younes, H. (2000). Coordina-tion for multi-robot exploration and mapping. Pro-ceedings Seventeenth National Conference on Artifi-cial Intelligence (AAAI-2000). Twelfth Innovative Ap-plications of Artificial Intelligence Conference (IAAI-2000), pages 852–8.W. Burgard, M. Moors, C. Stachniss, and F. Schneider(2005). Coordinated Multi-robot exploration. IEEEtransactions on robotics, 21(3):376–386.W. Kerr, D. Spears, W. Spears, and D. Thayer (2005). Twoformal gas models for multi-agent sweeping and ob-stacle avoidance. Formal Approaches to Agent-BasedSystems, 3228:111–130.

Map exploration using a line-based formation of mobile robots

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Map exploration using a line-based formation of mobile robots

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