Perception problem in robotics has been usually faced from the individual perspective, in this work we present preliminary results of a shared perception mechanism for a group of legged robots working is a dynamic scenario. The experiments has been conducted using a group of aiBo robots in the RoboCup environment. Each individual robot has its own perception of a common interesting feature, the ball, and they al together build a shared perceptio

Agüero, Carlos

González, Antonio L.

Martín Rico, Francisco

Matellán Olivera, Vicente

English

Leon University (Spain)

Distributed perception for a group of legged robotsCarlos E. Agiiero, Antonio L. Gonza'lez, Francisco Martin, Vicente MatellanRobotics Group. Departamento de Ingenieria Telemaitica y Tecnologia ElectronicaESCET - Universidad Rey Juan Carlos{caguero,antol,fmartin,vmo} @urjc.esAbstract - Perception problem in robotics has been usuallyfaced from the individual perspective, in this work we present SereoMirophonepreliminary results of a shared perception mechanism for a groupof legged robots working in a dynamic scenario. The experiments iumo-17aceLEDpahel IEEE 802.11bWireless LANhas been conducted using a group of aiBo robots in the RoboCupenvironment. Each individual robot has its own perception of a AccelerionSensorcommon interesting feature, the ball, and they altogether build a 3 J ,noshared perception. dtiEdge Delection :3ons Wireles olffSWtbhKevwords - Multi-robot, distributed perception SpeakerI. INTRODUCTION Figure 1. AIBO ROBOT WITH ITS MAIN SENSORS DETAILEDRoboCup [1] is an international joint project to promote Al,robotics, and related field. It is an attempt to foster Al and II. FROM LOCAL TO DISTRIBUTED PERCEPTIONintelligent robotics research by providing a standard problemwhere a wide range of technologies can be integrated and A repetitive task during a RoboCup game is to identifyexamined. RoboCup chose to use soccer game as a central topic and locate some elements, as for example the ball, the otherof research, with the aim of innovate to be applied for socially teammates, etc. In our team 1, each robot creates and maintainssignificant problems and industries. its own "map", where perceived elements have to be placed.In order for a robot team performs a soccer game, several Usually, robots do not manage crips positions, but fuzzytechnologies must be incorporated including: Perception of the estimations. So, we need to model positions as probabilityrelevant features of the environment (the ball, the goals, the distributions. This approach will be also used to mix local andteammates, etc.), multi-agent collaboration, real-time reasoning, remote information, and the mixed information will be anotherrobotics, and sensor-fusion. RoboCup is a task for a team of probability distribution.multiple fast-moving robots under a dynamic environment. Robots can also receive information from other team's robots.The competition is organized in various leagues according to In this case, we will need to mix locally perceived information,the type of robots used, their size, etc. The results showed in this and remote information. Each robot perceives the elements usingpaper have been used in the 4-legged category [2], where only its own sensors. So, estimated positions are always relative toaiBo robots are allowed. the actual position of the robot. We need to translate this localRoboCup 4-legged league is organized around two main information to global coordinates to mix it. First we need tocompetitions: soccer matches and technical challenges. Soccer translate the local polar perception (r, 4) to a global Cartesianteams are composed by four robots and they must play football map, as shown in figure 2.without the help of any external aid (human or computer). Only Local to global frame reference transformation is made inthe communication among the members of the team is allowed, two steps. First, we transform from polar to Cartesian referenceThis research has been partially sponsored by grants No. S-0505/DPI/0176 1 We are proud members of the TeamChaos, the team made up by threeby Community of Madrid and No. DPI2004-07993-C03-O1 by Spanish Ministry Spanish universities: University of Murcia, University Rey Juan Carlos andof Education corresponding to RoboCity and Acrace projects respectively. University of Alicante.1-4244-0830-X/07/$20.OO ©2007 IEEE.If we express the target pose in this way, we need to apply theTarget <e°° equation 4, in order to carry out the previous transformations.J corresponds to the Jacobian matrix of the covariance matrixto transform (Cm). To calculate the Jacobian matrix we need\ /-Xt to calculate the partial derivatives of f and g with respect to allvariables.Yt /Mnew-covar. JMJT (4)Therefore, if we start having a local object estimation basedin local and polar coordinates, we will need to fulfill two(Xr,Yr) transformations. The first one will turn the covariance matrix toRobotCartesian components (even in the robot system reference). TheFigure 2. LOCAL PARAMETERS second transformation will convert it in the covariance matrixrespect to the global reference system. The Jacobian matrix ofthe first transformation is showed in equation 5.Target O/f Of< J { Ar t3f \ K ~~~~~~~~~~~~~~~~~~~~~~~~Cos Q -rstin(fjg &g sin(X) rcos (())Ŷt\ 4 / ) The next Jacobian matrix is related to the last transformationx where are involved the whole of five variables: Three of themconcerning the pose of the robot (Xr, Yr, Or) and the last pair(xr,YE) pertaining to the position of the target (XT, YT).RobotOf Of Of of OfOg Xg 09 9&XT &YT &Xr aYr 00rXf Xtcos - GlobalRefer fc SystcmYtsin (K COS(0r) -sin(Or) 1 0 -Xrsin(Or)-Yrcos(Or) >(7)Sin(Or) COS(Or) 0 1 XrcOS(Or)-Yrsin(r) J)Figure 3. GLOBAL REFERENCE SYSTEM, AND PARAMETERS DEFINITIONFinally, to obtain the final covariance matrix, it is necessary toinclude the uncertainty in the robot pose (Cr) and the uncertaintysythem So, we of the target observation in local and Cartesian coordinates (CT)the equations 1. and finally to apply the multiplication matrix with its JacobianXT f (r, ) = rcos(q5) (1) and the transpose (fig. 9).lYT =g(r, 0) = rsin(o/) C 0)Second, we use the equations 2 to transform from local to C J(j[ )CrJ (8)global Cartesian reference system, as shown in figure 3.CT1,1 CT1,2 0 0 0X =f(XT,YT,Xr,Yr,or)= Xr +XTcos(Or)-YTsiTn(Or) (2) CT2,1 CT2,2 0 0 0l =Y=g(XT,YT,Xr,Yr,Or)=Yr+XTSiTn(O,r)+YTCOS(Or) j 0 0 0 0 0 TJ (9)In order to incorporate the uncertainty, we will represent the 0 0 0 Cri1, Cr2position of an element as a normal dIstribution represented by the 0 0 0 Cr2,i Cr2) /covariance matrix shown in equation 3 , where (ag, Q) represent II XEIETTO NIOMNthe error in the estimation of the distance and angle. Our tests II XEIETTO NIOMNwith the observation model revealed that the error in the distanceestiatin islinar ( * ) an th errr i theange ca be Robot localization is a very challenging problem. Neverthe-consideredconstant(Q). ~~~~~less, the purpose of our experiments is to validate a solution forthe shared multi-robot perception problem. We have installedC _ t Sr° 8 _ t (oUr) 0 8 3 a groundtruth system [3] in our laboratory. In this way, wem-t00 J-t ° S2 J ~~(3) can forget the problems caused by the self-localization of therobots in the shared perception experiments isolating our task IV. BALL SHARINGfrom others related.In figure 4 we can see the calibration interface of the A typical behavior at RoboCup environment is to look for theball. This task is basically composed by a set of head movementsgroundtcolruthbprogram[3] used to adjustthecameraparameterstravelling the field combined by some kind of heuristic for theand color tables.robot's movement. In a typical match there are eight robotsplaying on a 24m2 field. It means that we have a large field. VW DAMN . llwithlots of moving objects hiding the ball.We think that we can improve the time that a robot needsto localize the ball using shared information by its teammates.In our team, each robot should be spread on the field runningdifferent roles: Goalkeeper, defender, kicker and supporter.Goalkeeper remains in its area, defender stays in a defensivezone near its own net, kicker will be near the ball and supporterbi-, ll1!!! _ - 1will be located on the attacking zone. It means that we have ateam covering a large surface of the field. If they do not shareany information, normally one or two of them will be trackingthe ball but the others will be wasting their time looking for it.A real team in any environment uses its members ina cooperative way. We think that we can exploit ourcommunication features to share the ball position and maintain aglobal state of the ball pose among all the team members. Also,Figure 4. MEZZCAL INTERFACE a robot could know the pose of the ball even if a set of robotswill be hidden the ball.Figure 5 shows a capture of the application tracking three Every robot in our team maintains a data structure calledrobots in real time. Each aiBo robot is equipped with an LPS (Local Perception System). LPS stores the current localunique colored label for allowing object identification. We have estimation to every interesting object of the field (landmarks,developed a small program that reads the output generated by the nets and ball). The local estimation is a pair of numbers withgroundtruth (a set of poses related to each object detected) and the distance in millimeters, and the angle in degrees to the objectsends individual localization information to each robot. measured from the robot system reference.The local localization system in each robot is overwritten by In order to use the shared approach in our team, we havethe information coming from the groundtruth. By this way, we included a new data structure for shared information. Now, everyobtain a very good quality localization in a transparent way. robot can consult its local information or the shared information.Note that the shared data does not replace the local information.In some cases, local estimations will be better and more precise.The shared information is a complement to local one.Team cooperation is a key point in elaborated tasks. Lastyear, our team has been working in a system called Switch! [4],that allocates robot roles in a dynamic way according to the playconditions, as we mentioned before. Every robot must send tothe other teammates its information related to its position in thefield and some local estimations to the objects. According to theinformation received and each own data, every robot choose oneof the available roles and executes its associated behavior.For this work, we have used the previous infrastructureFigure 5. MEZZANINE TRACKING THREE OBJECTS developed for Switch! and include in the messages exchangedthe pose and uncertainty of the ball. Now, when a robotW haeud receives information from a teammate, it can merge its own ballestimation with the estimation received from the other robot.[11] for the experiments. Team Chaos environment includes Th mege esiato ithreutocmbngtwseveral modules for solving locomotion, visual perception, oainemti ucranyo ahbl siain n wcommunication, amongst others.poionetminsThe higher level behaviors module has been slightly modifiedfor sending/receiving local information periodically. Each robot C =C1 -C1 [C1 + C2] 1 C1 (10)will use this information to create a global shared state that willbe used in case the behaviors need it. X=X1+C1[C1+C2] 1(X2-X1) (1I1I)Both estimations are merged into a new one by multiplying ball represents the local estimation of robot 2, and the blue ballthe distributions. We have used a two-dimensional statistical corresponds to the shared one. Note that the situation showed onapproach based on Bayes' Rule and Kalman filters explained in the screen does not correspond with the scene showed in figure[5]. 6.We triggered the log caption of the robot 1 from theV. EXPERIMENTAL RESULTS ChaosManager and after 10-15 seconds we stopped the log.Then, we changed the position of the ball to the position 2In order to test and measure the quality of the shared and repeated the process. At the end of the test we stored 20estimation to the ball we have made some tests. One side of files with the log of each test. In tables I and II we can seethe typical RoboCup field has been selected for making the the data associated to each point. The error is represented asexperiments. First of all, we have marked on the green carpet the Euclidean distance from the ideal position of the ball to theof the field the position of 20 different points. estimated position.Next, we have put a pair of robots in two fixed positions, RI(0,0,-~) and R2 (-1800,-1350,0) as you can see in figure 6. Point Distance RI Error RI Std. Desv. Error1 2102.97 412.40 477.842 1916.37 101.84 82.01...... .3 1850.0 91.43 51.034 1916.37 151.75 114.186 1680.02 93.54 50.887 1439.61 51.92 19.668 1350.0 26.62 18.499 1439.61 43.85 21.0210 1680.02 95.73 68.341 1 1312.44 62.90 10.7112 986.15 110.40 15.6013 850.0 29.54 6.7414 986.15 28.25 10.6315 1312.44 31.16 20.1416 1077.03 109.70 8.3517 640.31 51.37 3.6918 400.0 24.44 1.7419 640.31 10.03 2.4920 1077.03 69.29 7.38Figure 6. SCENE OF THE TEST WITH BOTH ROBOTS TRACKING THE BALL TABLE I.LOCAL ESTIMATION AT DIFFERENT POSITIONSWe began putting the ball on the point 1 during 10-15 secondsapproximately. Our robots were running a log system activatedfrom our suite of tools called ChaosManager. Also, the robotswere receiving the ground truth information and they were [Point Distance R2 IError sh-a-re-d Std. Desv. Error sharedupdating their localization modules. 1 2844.29 582.15 433.702 2353.72 252.81 237.763 1868.15 106.16 39.624 1392.83 63.02 38.08520 11 1241943.39 149.12 192.96 2800.0 203.57 276.56El.6MI&C,~~ ~ ~ -7 2300.0 82.30 52.708 1800.0 53.61 14.939 1300.0 36.87 18.205~ ~~ ~~ 11 .I774 3 3 2 9 ~~~~10 800.0 36.78 28.4012 2353.7 144.49691Figure 7. CHAOSMANAGERSHOWING LOCAL AND SHARD ESTIMATIONS7 TABLE I13 1868.15 49.59 22.7314 1392.83 46.89 6.3315 943.39 18.59 11.4416 2956. 77 184.15 398SHARED ESTIMATION AT DIFFERENT POSITIONSWe can see a graphical representation of both local and sharedestimations on the ChaosManager as shown figure 7. The orangeIf we fuse the raw data of the tables into a single graph we In our approach, we have isolated the uncertainty incan see how is the precision of the shared estimation compared localization, but this method allows to easily integrate the errorwith the local one. In figure 8 we can see both estimations. in location of each robot.The location of the robot 2, which is located at larger distance Future lines will be addressed to combine more than twotowards the ball than robot 1, makes that shared estimation is robots and to study different techniques to weight every databetter than local in six points of the twenty measured. If the received, according to different parameters.teammate of the robot 1 were located closer to the ball than robot1, the shared estimation would be better in all cases. ACKNOWLEDGMENTSAuthors would like to thank the TeamChaos and Robotics Labmembers for their support.Local and shared estimations1200 r 1 T T 1 Local error REFERENCESShared error1000 a [1] H. Kitano, M. Asada, Y. Kuniyoshi, I. Noda, and E. Osawa, "Robocup:The robot world cup initiative," in ICJAI-95 - Workshop on EntertainmentE soo and AI/ALIFE, 1995.[2] The Robocup Federation, "Robocup Four-Legged League,"a http://www.tzi.de/41egged/bin/view/Website/WebHome.Q 600E [3] U. R. R. Lab, "Mezzanine," 2007,http://playerstage.sourceforge.net/mezzanine/.[4] C. E. Agiiero, V. Matellan, V. G6mez, and J. Canias, "Switch! Dynamicroles exchange among cooperative robots," in Proceedings of the 2ndt 200 . International Workshop on Multi-Agent Robotic Systems - MARS 2006.INSTICC Press, 2006, pp. 99-105.0 11~~~~~~.77~[5] A. Stroupe, M. C. Martin, and T. Balch, "Distributed sensor fusion forobject position estimation by multi-robot systems," in IEEE International__20 _ _ ,, Conference on Robotics and Automation, May, 2001. IEEE, May 2001.0 2 4 6 8 10 12 14 16 18 20 [6] Sony, "Sony Global - AIBO Global Link,"Point in which is located the ball http://www.sony.net/Products/aibo/.[7] J. Canovas, K. LeBlanc, and A. Saffiotti, "Robust multi-robot objectlocalization using fuzzy logic," in Proc. of the Int. RoboCup Symposium,Lisbon, PT, 2004.Figure 8. LOCAL AND SHARED ESTIMATION USING TWO ROBOTS [8] Y. U. Cao, A. S. Fukunaga, and A. B. Kahng, "Cooperative mobilerobotics: Antecedents and directions," Autonomous Robots, vol. 4, no. 1,pp. 7-23, March 1997.Despite the location of the robot 2, the shared estimation [9] K. Lerman, C. V. Jones, A. Galstyan, and M. J. Mataric, "Analysis ofimprove the accuracy of the estimation of robot 1 in all cases dynamic task allocation in multi-robot systems," International Journal ofRobotics Research, vol. 25, no. 4, pp. 225-242, 2006.in which the distance between the ball and robot 2 is closer that [10] Sony, "Sony AIBO SDE and Open-R SDK," http:Hopenr.aibo.com, 2006.the distance to the ball and robot 1. This information is available [I1] TeamChaos, "Team report 2005," 2006, online at www.teamchaos.es.to every robot when it receives data from other teammate, so it [12] C. E. Agtiero, F Martin, H. Martinez, and V. Matellan, "Communicationsis k t mi d rand basic coordination of robots in TeamChaos," in Actas VII Workshopis easy to know if the shared estimation could improve the local de Agentes Ffsicos, 2006, pp. 3-9.one. [13] B. Gerkey and M. Mataric, "On role allocation in RoboCup," in RoboCup2003: Robot Soccer World Cup VII, 2004. Springer-Verlag, 2004, pp.43-53.VI. CONCLUSIONS AND FURTHER WORKIn this paper we have shown the preliminary results of amethod for sharing information about relevant elements of theRoboCup environment. This technique could also be usedin many other environments, where perceptive information isreceived from different sources.This method is based in Bayes' rule and Kalman filter theoryand can fuse two-dimensional Gaussian distributions into asingle one, contributing more accuracy in some cases.In a typical soccer RoboCup match, it is very common that agroup of robots hide the ball to another. With this approach,every robot can maintain a parallel estimation that not onlyimprove the precision, even in some cases will be the only wayto locate the ball due to occlusions in the play. In that case theoverall gain is infinite, due to with its local estimation the robotcan not see the ball.

Distributed perfection for a group of legged robots

https://buleria.unileon.es/bitstream/10612/2092/1/Carlos.pdf

Distributed perfection for a group of legged robots

Abstract

Similar works

Full text

Available Versions

Leon University (Spain)