Bemb e l b o t s F r a n k f u r tRoboCup SPL Team at Goethe University Frankfurt

Dipl.-Inf. Markus Meissner1, Dr. Holger Friedrich2, Dipl.-Inf. Andreas Fürtig1,Tobias Weis2, Jens-Michael Siegl1, Christian Becker1, Vincent Michalski1,Dipl.-Inf. Linda Luy1, Gerhard Ruscher1, B.Sc. Andreas Kehlenbach1, Dipl.-Math. Anna Reckers1, Konrad Zacharias1, Eric Hanssen1, and Alexander Heun1

1Goethe University, Frankfurt, Germany2Frankfurt Institute for Advanced Studies, Frankfurt, Germany

teamleader@bembelbots.de

1 Statement of CommitmentTeam Bembelbots Frankfurt applies for participa-tion in RoboCup 2013 SPL.

2 Team BembelbotsThe Joint Robotics Lab (JRL) is a joint project ofthree working groups of the Institut of ComputerScience at Goethe University Frankfurt with thecommon goal to strengthen project orientated learn-ing.Early 2009 the JRL offered a working group, calledRoboCup-AG. Within this group up to ten stu-dents started developing a robot behaviour usingthe Simulator Webots1. Still in the same year thisinitial team founded the RoboCup team BembelbotsFrankfurt. The whole team was very motivated al-ready from the beginning, met several times a weekand everybody spent a lot of free time to performstrongly. This was necessary as right from the start,we decided to build our own framework from scratchand not to using already existent frameworks.Due to fast satisfying results we participated in theRoboCup German Open, already in 2010. Sincethat time, we took part every year, gaining a lotof experience. We also obtained new practicaland theoretical knowledge through visiting OpenRoboCup Workshops in Berlin, through playingfriendly matches and through exchanging knowl-edge with other teams. Thereby we made contin-uous good progress improving our framework. Weimplemented a robust vision, a satisfying self locali-sation, a dynamic behaviour, an own walk and muchmore. As a result of our efforts we got the chance toparticipate in the RoboCup World-Championshipin 2012 and enjoyed the experience of our first in-

ternational competition very much.During the years the team members changed butoverall it grows continuously.

Figure 1: Team Bembelbots at the RoboCup German Open2012 in Magdeburg.

Team Structure: Team leader of Bembelbots isMarkus Meissner, a PhD student at the ElectronicDesign Methodology group at the Institute of Com-puter Science at the Goethe University Frankfurt.He has a good overview of the team members’ activ-ities and is very avid, especially in the developmentof behaviour and motion. Dr. Holger Friedrich, for-mer head of the team, has left Goethe University,but still spends a lot of his free time for the team, es-pecially in the areas vision, framework and softwaredevelopment methodology. Andreas Fürtig just fin-ished his studies and wrote his diploma thesis oncolour classification [2]. He will become a dedicatedPhD student at the Joint-Robotics-Lab, soon. Wehave several senior students with strong contribu-tions to the RoboCup. Tobias Weis has finishedhis diploma thesis on self localisation [3] and is stillworking continuously on improving the localisation.

1http://www.cyberbotics.com

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Christian Becker is currently working on his thesison advanced line detection and scan-matching al-gorithms. Jens Siegl contributes technical knowl-edge and maintains the robots since the beginningof the project. Andreas Kehlenbach provided theinfrastructure for XABSL and works on enhancingthe walk-engine. Linda Luy and Vincent Michal-ski develop behaviours and Gerhard Ruscher con-centrates on motions. Anna Reckers is the contactfor mathematical problems. In addition we haveseveral students who joined the RoboCup team inthe last years. Konrad Zacharias, Eric Hanssen andAlexander Heun recently joined the team and arestill searching for their field of interest.

3 Inventory of NAOsWe currently own six Nao V4 H21. As soon as anew version will be released, we will upgrade ourNAOs. At the moment we do not intend buyingany further robots.

4 Research Interests and PlannedActivities

Focusing on computer vision development in thepast years, the team Bembelbots made fast progresstowards a reliable image based self localisation andtherefore was also able to enhance the robots be-haviour concerning game strategies.In the last years the Visual Sensorics and Informa-tion Processing group concentrated on developing arobust vision system for the Nao. As an addition toTobias Weis’ diploma thesis, which dealt with ob-ject recognition, the optimization of backprojectedline models and localisation in general [3], ChristianBecker is working on his diploma thesis concerningpositiontracking via scanmatching. The results ofboth works provide a lot of features needed for im-proving of the Multihypotheses Kalman Filter algo-rithm used for localisation.The far higher performance of the Atom processorwhich is now integrated in the new Nao model, al-lows the reasonable usage of both cameras avail-able. They provide a wider viewing range and fur-ther research may lead to stereo vision approaches,although recent evaluation showed that there aresome technical limitations introduced by the Naorobot.The forthcoming modification of the RoboCup fieldsize led to considerations for making the field sizescalable. Due to restricted space in the team’s lab,

this project made fast progress and only slight mod-ifications will be needed to successfully vary be-tween any given sizes in the future.Past experiences showed that using the providedNaoQi framework is not the optimal solution forcompeting against teams who detached their selvesfrom this framework: faster walks and an overallimproved agility are promising enhancements thatlegitimate the development of a self-made motionengine. Hence we started to disengage from theNaoQi and developed our own walk and are takingfurther steps to develop a completely self-containedmotion engine.

5 Summary of Past Relevant Work5.1 Software FrameworkWe developed our own software framework fromscratch. It is written in C++ and consists of sev-eral modules. We compile against the NaoQi mid-dleware, but have minimized the dependencies andwrapped library dependent calls. In addition toNaoQi we use external libraries OpenCV, libbzip,Boost, and the XABSL engine. For the future weare planning to amplify the split-off of the NaoQifrom our framework. The following paragraphs givean overview of the key aspects we focused on duringthe last three years.

5.1.1 Development Methodology

We have a strong focus on automated testing. Ourbase software framework is compiled and tested ev-ery time new code is checked into our source codemanagement system. The unit test framework weuse is googletest2, and we check the coverage of ourtests with a profiling tool gcov from the GNU Com-piler Collection suite. Continious Integration withJenkins builds the whole framework on different sys-tems evertime the codes is changed and ensures astable framework at every time.

5.1.2 Locomotion

Walking Engine: Our first implementation for anown walk was based on [6]. As the paper proposed,we implemented a closed-loop walk.However the stability of that walk was too bad forreal walking. Our robots were able to dribble, butnot to walk. Further tests and literature researchguides us to an open-loop walk engine.According to that and with the helpful assistanceof Team Nao-HTWK of Leipzig we successfully de-veloped a full parametrizable walk implementation

2http://code.google.com/p/googletest/

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during the last year. A joypad control has beendeveloped to comfortably setting the parameters.

Standing up: There are many reasons why a robotmay fall over. In this case it is very important forthe NAO to stand up again. Thus right at the timeof our teams’ initiation, we developed motions forstanding up from different positions. These motionsworked very safe but also very slow, so we enhancedthem to become faster.

Goalie Move: Now that we have a reliable local-isation, we implemented a dive for the goalkeeperthat throws the Nao to the ground to cover muchmore space than with the previous one. After Ger-man Open 2011 we started developing a move topick up the ball with the hands and throw or kickit away again. As soon as the rules are changedaccording to the ball holding rule, we will use thismotion with our goalkeeper.

Kick: We implemented three kinds of kicks: front,back and side kick. The Nao chooses the type ofkick due to the implemented behaviour. In addi-tion, each kick is parametrizable, where the robothimself computes the right parameter from the ballposition.

5.1.3 Cognition

Camera Like most teams, for a long time we usedonly one camera. Regarding the increasing field in2013, it will become more necessary to have a far-reaching view. Seeing the goal at the end of the fieldis as important as seeing the ball directly in front ofthe robots’ feet. That’s the reason why we decidedto use both cameras and we already implementedthe usage.

Classification The cognition process is mainlysplit into two parts. In a first step, colour classifica-tion is applied to the high resolution image. Clas-sification is based on a Gaussian model describingthe statistics of the colour values. A fast implemen-tation using Lookup Tables guarantees a process-ing rate in real time [1]. After this step, the envi-ronment is separated into 6 different colour classes,which can easily be processed in order to find impor-tant elements on the field. The classification step isvery robust to changes in illumination, which typ-ically appear in competitions or show events, andthus there is no need to calibrate the Lookup Tableby hand before each game.To deal with the new field dimensions, we decidedto use both cameras of the Nao. Both images are

not processed in parallel, instead we toggle the cam-eras in each step to save computational power. Dueto a high framerate of nearly 30 processed imagesper second, this means we classify 15 images of eachcamera per second.To minimize cache-misses, which lead to a slowerprocessing rate, the whole Lookup Table was alsocompressed [2]. A result image of the classificationstep can be seen in Fig. 2.

Detecting Objects Based on the fact that thecolour-classification provides very good results, thesubsequent object recognition modules work on bi-nary images of each colour class of interest. Mainlyhistograms on this binary images are used to findobjects of interest, like goals and balls. This methodcomes with the benefit of being very robust on asmall number of misclassified pixel values.Besides simple geometry checks on the shape of theobjects to be recognized, a feedback loop has beenimplemented relating the distance of an object toits dimension. After calibration of the robots, weachieved good results for distance measurementsbased on triangulation.

Figure 2: Result of the Dual Vision Module. Left column:Original Image (converted to RGB), right column: Classifi-cation of the input image with found vision results: A ball,yellow goalpoles and several corners, which then are processedby the localisation module.

Detecting Lines, Corners, and the Centre CircleThe colour classified images are scanned with hor-izontal and vertical scanlines to find green-white-green transitions. Currently we are developing amethod, that not only takes colour into accountand makes use of en- and decreases in the illumi-nation channel of the YUV -image. The scanlinesdistance is adjusted based on the distance of imagepoints projected to the ground-plane, such that thedistance between scanned points on the ground isless than 5cm.

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Detected line points will be connected to lines,and in a second step lines that match in spaceand orientation will be joined. These lines arethen projected onto the ground-plane of the robot-coordinate-system. In this space, known geomet-rical relations between field lines are apply, easingthe detection of then centre circle and the corners.

5.1.4 Localisation

During the past year our main focus of attentionwas the improvement of our localisation methods.The detection of lines, corners, the centre circle andpoles (as explained above) are used for computingall likely positions of the robot in the world.

Figure 3: Top: The soccerfield underlayed with a voronoimap. Each colour corresponds to a fieldline. This map isused for lookup. White the detected fieldlines, projected tothe ground at the believed robot position (green).Bottom: Magnified view after the Scan-Matching process.The grey dots represent the former detected lines. It is easyto see that the overlay is much better. Red is the correctedposition on the field.

The more features detected in one picture or themore expressive the detected features are, the lesspositions are likely (a detected goal equals two likelypositions on the field, where a single L-Corner yieldseight possible positions). Every possible positioncalculated like this is a measurement for the Kalmanfilter. However, the benefit of different colouredgoals is no longer existent and the feasibility of fol-lowing more than just one track is a crucial con-straint.

The additional symmetries introduced by two goalsof the same colour implicate an increasing varietyof robot positions for a single measurement. Theincreasing number of field players further decreasesthe possibility of detecting strong landmarks, so weimplemented a Multi Hypothesis Kalman Filter andintroduced two new measurements for breaking thesymmetry.The use of infrared emission by the goalkeeper aidsto the localisation of field players by providing astrong hint about their orientation and about theirfield half (IR range is approx. 3m). Fused ball po-sitions (especially with the goalkeeper) also provideuseful information about the field half, and, in thecase of a successfully fused team-ball, about theirpositions.For cases where little information is available forlocalisation (e.g. only one line), we are developinga Scan-Matching approach based on [9] to providesome kind of local position estimation.

Figure 4: Live visualisation of the hypotheses of the kalmanfilter with according covariances. The direct result of thecurrent measuremnt (goal) is shown in pink, the most likelyhypothesis in red.

The detected field lines and other features will bematched to the known environment, given the po-sition estimate. To minimize the lookup of whichdetected feature should be matched to which of theworld we use a similar approach like [10]. This willallow position correction even with very little infor-mation. Fig. 3 shows an example.

5.1.5 Behaviour

Since our first participation in the German Openwe have full support for the Game Controller andthe Button Interface. At the beginning, we usedhard coded finite state machines to control the be-haviour. As our team has grown larger, we de-cided to divide the development into pure frame-work development and behaviour development. Toget this working, we’ve chosen the Extensible Agent

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Behaviour Specification Language (XABSL) as be-haviour development language. We integrated theXABSL engine into our framework and imple-mented a wrapper class, to parse arguments fromthe framework to the behaviour and vice versa.

5.1.6 Scaleable field size

Based on the announcement to enlarge the field in2013 we will no longer be able to construct a fieldin one of the institutes rooms. Therefore we re-structured all modules and external tools to utilizea centralized variable field definition. This also al-lowed the construction of a smaller, permanent fieldin one of our lab rooms.

5.2 ToolsSeveral tools have been developed in the last years,allowing us to debug and analyse the behaviourof our Naos. Those tools are written in Python,mainly for portability purposes. They work eitherwith Linux, Windows and partly also on mobilephones (Android). NaoDebug is the main Applica-tion for debug the Robot, for example change cam-era parameters or receive live images. The currentstate of the XABSL FSM can be displayed graphi-cally, as well as the current state of the world modelof the robots and their global position in the world.This speeds up the whole development process, aserrors are detected very early and easily.Our old remote tool to control the robot for demon-stration purposes was merged with NaoDebug. Anold version of the remote (NaoRemote) is still avail-able on the team home page [7].Since all of our tools still use the connectivity ofthe NaoQi Middleware, we plan to implement anabstract connection layer to communicate with ourrobots over a standard TCP/IP Connection in thefuture.

5.3 Public ActivitiesTeam Bembelbots is also very devoted to increasethe awareness of the RoboCup initiative by orga-nizing and taking part in public events.Every year we are organizing different events forthe high frequented Night of Science, since 2011 weare also part of the Computer Science Day and theSummer Festival of Goethe University. In 2012 theteam carried out robotic classes for high-school levelstudents participating in a summer school programprovided by Goethe University. Several positive re-views in local and regional press documented thegreat success of our public activities3.

Figure 5: High-school students working with the Nao in asummer school.

5.4 Scientific PublicationsScientific publications [1], Diploma theses [2, 3],Bachelor theses [4, 5].

References1. Fürtig, Andreas; Friedrich, Holger; Mester,

Rudolf (2010): Robust Pixel Classification forRoboCup, Farbworkshop Ilmenau

2. Fürtig, Andreas (2011): Farbklassifizierungfür humanoide Roboter im RoboCup Umfeld,Diploma thesis at Goethe-University, Frank-furt

3. Weis, Tobias (2011): Selbstlokalisierung fürhumanoide Roboter im RoboCup Umfeld,Diploma thesis at Goethe-University, Frank-furt

4. Rohnfeld, Erik (2010): Reinforcement Learn-ing zum Lernen eines Schusses auf demNao-Roboter, Bachelor thesis at Goethe-University, Frankfurt

5. Jäger, Moritz (2010): Entwicklung eines au-tomatisierten Verfahrens zur Optimierung derLaufbewegung des Nao Roboters, Bachelorthesis at Goethe-University, Frankfurt

6. Behnke, Sven (2006): Online Trajectory Gen-eration For Omnidirectional Biped Walking

7. NaoRemote for Nao Robot (2012):www.bembelbots.de/web/robocup/downloads/

8. Quinlan et al.:The 2005 NUbots team report.Technical Report, The University of Newcas-tle, 2006.

9. Cox, Ingemar J. (1991): Blanche - An Ex-periment in Guidance and Navigation of anAutonomous Robot Vehicle

10. Whelan, T. et al (2012): Line point registra-tion: A technique for enhancing robot local-ization in a soccer environment

3http://www.bembelbots.de/web/press/

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