International Conference on Advanced Robotics, Coimbra, Portugal, Julho 2003This paper describes the control and localisation design
and implementation status of the ISePorto robotic football
team for participation in Robocup Middle Size League
(F2000). The objectives guiding the project were the
applications and research in hybrid control and
coordination systems. The system has also an educational
support role. A special attention is made to the custom
design to allow the execution of complex manoeuvres and
team coordinated behaviours. The robot has different
pass, shot, and manoeuvre capabilities providing high
level tactical and strategic planing and coordination

Almeida, José

Martins, Alfredo

Silva, Eduardo

Repositório Científico do Instituto Politécnico do Porto

See	discussions,	stats,	and	author	profiles	for	this	publication	at:	http://www.researchgate.net/publication/228738901Vision,	Localisation	and	Control	of	the	ISePortoRobotic	Soccer	TeamARTICLECITATIONS2READS154	AUTHORS,	INCLUDING:J.M.	AlmeidaInstituto	Superior	de	Engenharia	do	Porto46	PUBLICATIONS			153	CITATIONS			SEE	PROFILEAlfredo	MartinsInstitute	for	Systems	and	Computer	Engine…50	PUBLICATIONS			240	CITATIONS			SEE	PROFILEE.	Pereira	SilvaInstituto	Superior	de	Engenharia	do	Porto62	PUBLICATIONS			199	CITATIONS			SEE	PROFILEAvailable	from:	J.M.	AlmeidaRetrieved	on:	23	October	2015Control  and Localisation for the ISePorto Robotic SoccerTeamJ. M. Almeida, A. Martins, E. P. SilvaLSA -Autonomous Systems LabInstituto Superior de Engenharia do Porto R. Dr. António Bernardino de Almeida 431 4200-072 Porto, Portugal{jma, amartins, eaps}@dee.isep.ipp.ptAbstractThis paper describes the control and localisation designand implementation status of the ISePorto robotic footballteam for participation in Robocup Middle Size League(F2000). The objectives guiding the project were theapplications and research in hybrid control andcoordination systems. The system has also an educationalsupport role. A special attention is made to the customdesign to allow the execution of complex manoeuvres andteam coordinated behaviours. The robot has differentpass, shot, and manoeuvre capabilities providing highlevel tactical and strategic planing and coordination.1. IntroductionThe ISEP Autonomous Systems Lab. (LSA) roboticfootball team provides an excellent tool to develop anddemonstrate the research in the areas of interest associatedwith autonomous systems. These are mainly sensorfusion, mobile robotics navigation, nonlinear hybridfeedback control and coordination. Additionally to theresearch interests, the laboratory has a strong educationalpurpose, being the robot team a good support to curricularand extra-curricular work in the areas of mechatronics,electronics and embedded systems.The remaining of the paper overviews the robot design,the control and navigation issues, coordination andstrategy guidelines finishing with the current team status.2. Robot DesignThe team robot was designed and implemented fromscratch in order to be a suitable testbed for advancedmultirobot coordinated control. The mechanical designhas taken in account the requirements to execute complexmanoeuvres and therefore not posing harsh limits on thecontrol, navigation, or coordination developments.The robot is constituted by three parts: a circular mobilebase, a kicker connected to a structure that rotates arounda central vertical axle and on top a computational anelectronics module fixed relatively to the base with a panmounted camera. The system is mechanically modular; itcan be used in different configurations, such as differentkicker designs with the same base. The base contains twodifferential traction 24 V DC motors with optical encodersfor motor control and vehicle odometry, two 12V leadacid batteries, the kicker rotation motor and the motorpower drives. The kicker uses a DC motor and mechanicalspring with a camber and was designed to allow differentkick strengths ranging from small passes to goal shots.This, coupled with the rotation allows the robot toperform complex manoeuvres.The main computational system is consists of a 5.25”SBC (ICP NOVA7896FW with a 900Mhz Celeron) andIDE 24Mb Flash disk. Each robot communicates with theteam and the host visualisation computer by an ethernetwireless modem (OTC AirEsy2405 at 2.4 GHz), to bechanged to new modems compliant with IEEE 802.11.The motor control is made in a custom designed multi-axis control board, comprising a FPGA (FLEX 10K10)and dedicated microcontroller (T89RD2). Itcommunicates with the main CPU trough a PC104connector (ISA bus). The PC104 form factor reduces sizeand it is a reliable connection system. The boardimplements 4 axis PID control at 2 KHz (traction, kickerrotation and shot), receiving encoder information andproviding a sign magnitude PWM control signal to thepower drives. Additionally, this board interface amagnetic compass (Vector 2X), and an optical switch tocalibrate the angle of the rotating kicker with respect tothe robot base. The robot has also a custom developedring of IR range measuring sensors for short distanceobstacle detection (up to 0.8m) .3. Development SystemThe onboard computer runs a Linux operating from theflash disk, with a modular and hierarchic threadedsoftware architecture [3], [4].A specific distribution was developed for the vehiclesand the standard Linux kernel was modified in order toprovide some real-time functionalitities and additionaldevelopment tool support.These modifications include:• High resolution clocks• Preenptive Kernel• Linux-Trace Toolkit SupportIn addition a remote variable inspector and logging toolis being developed.4. Vision SystemThe vision system uses two USB cameras (PhilipsPVC740K with a new wide angular lens from MarshallElectronics optics). One mounted on a central pan unitand used mainly for long range vision and localisation andthe other fixed to the kicker to be used for fine ballcontrol.Device DriverVideo4LinuxColor classificationObject detectionHardware(USB, camera)Sensor ManagerRequest  Vision dataOperating System (Linux) Hardware Vision SystemFigure 1 -  Vision system architectureThe colour classification algorithm uses a pre-determined set of regions in the YUV in order to detectthe game relevant colours. This process is still verysensitive to lighting conditions. In the following image asingle colour calibration is compared with the multiplecolour classification needed for the system.Figure 2- Single colour calibration and multiple colourcalibration (kicker camera).As can be seen, there are overlapping regions in thecolour space. In the second case, some previously wellclassified points are wrongly classified. It exists a trade-off between high computing time and precision in thecolour classification that imposes harsh restrictions on thevision system capabilities.The main characteristic of our vision system is theability to perform a fast global analysis of the all image inorder to decide where to conduct a more detailed one. Anadditional improvement is accomplished with theintegration of the prediction the landmarks in the grabbedimage or where the camera should point.The object detection is performed on the colour-classified image.  The objects classified are:- Ball- Corner flags- Goal- Team colour marks- ObstaclesThe ball is detected using a blob based algorithm. Thisprovides information regarding ball centroid andbounding box for all possible clusters.The other objects are detected along specified scanlines. An hierarchical approach is taken to eliminate falsedetections.Figure 3 – Horizontal scan line in the original image(head camera).Due to wide angular characteristics of the lens used(needed for convenient field of view) the image receivedhas a significative distortion. This distortion exists in bothcameras but is more relevant on the kicker camera.The distortion is taken in account in the scan lines. Forexample a horizontal line (see the blue line in the previousfigure) is computed in the distorted space, and the result isused in the scan. The information obtained in the distortedspace is then converted to a corrected image coordinates .5. LocalisationIn the Robocup to achieve good team play capabilities,is required a good robot localisation and control.Besides the robot self-localisation it is also necessary toperform adequate ball (and if possible other players)localisationThe ball position estimation uses a Kalman filter takingin account the robot model and the information receivedfrom the vision system. Adaptive ball detectionalgorithms are used for different cases of ball position andoccultation in the image.Figure 4 - Different ball position and partial viewwithin the image frame.An updated ball position estimate is maintained in therobot internal state.BallDetectionXball ref.cameratoXball ref. robotBall stateestimationballPballXrobotProbotX,ˆ,ˆXball ref. cameraXball ref. robot (timage)worldinfoballball PX ,ˆimageFigure 5 - Ball localisation process.The self-localisation is mainly done by the fusion ofvision measures of world landmarks (goals, corner andground marks), internal sensors (odometric and magneticcompass) and external vision measures, using Kalmanfiltering methods.Figure 6 – Corner and goal vision dataIn the previous figure, we can see a 2D representationof the raw data measures of the angular and distance ofthe 4 corner flags, and two goals.For the corner flags, we can see the result of using theangular velocity and an estimated delay in the imageacquisition to correct the angular of the corner measures,measures draw in yellow and blueIn the next table, are shown some statistics for the allthe corners.AngularstatisticsE(θ_corr)STD(θ_corr)E(θ_w0) STD(θ_w0)E(θ) STD(θ)BYB1 23.83 0.77 24.11 0.37 23.80 2.39BYB2 -21.06 0.84 -21.35 0.61 -21.22 2.30YBY1 132.92 1.31 132.84 0.32 132.24 7.62YBY2 224.48 1.33 224.16 0.28 225.42 7.48Distance E(d) STD(d) E(d_w0) STD(d_wo)BYB1 6.54 0.29 6.50 0.20BYB2 6.54 0.50 6.74 0.48YBY1 3.71 0.13 3.65 0.04YBY2 3.39 0.10 3.36 0.08Table 1 – Statistics for corner measuresIt can be observed in the previous table the statistics forthree scenarios. Each line corresponds to a differentcorner . The situations analysed for the same data are, theraw measurements statistics (E(θ), STD(θ) ), the subset ofmeasures taken with null camera angular velocity(E(θ_w0), STD(θ_w0)) and the overall correctedestimates taking in account the camera angular velocityand delay in the image capture.We plan to test fusion algorithms based in covarianceintersection [6].Furthermore, each robot have a world state with someknowledge of position, attitude and his derivatives, withsome uncertainty measure for all the game movingobjects. This is accomplished with distributed sensorfusion, where the vision sensors play a key role insensing. Also, a distributed dynamic camera allocationand managing is under study.In the presence of communications problems, thedesired information must be perceived individually byeach player. In the last case, in spite of an obviousdegradation of the world model, some team coordinationmust be accomplished by the perceived information of ourrobots.6. ControlThe motion control architecture approach [1], [7] isbased in atomic parameterised hybrid feedback controller,also known as manoeuvre. These controllers incorporateboth continuous and event driven feedback. This approachinvolves the atomic parameterised hybrid controllerssynthesis. A set of manoeuvres solving specific classes ofmotion problems is defined and implemented. These areclassified according to the patterns of the associatedconstraints and objectives. Those manoeuvres are theresources to the coordination level. The sets ofmanoeuvres that are being synthesised are:Motion without ball:- Move to location avoiding obstacles- Block goal path- Approach ball with defined attitude- Move to maximise target vision information- Goalkeeper block goal pathCoordinated robot and kicker motion:- Smooth ball receptionBall guidance:- With robot rotation around ball centre- Straight line- Curve- With directional shoot and defined strength- Interception and kick- Lateral pass with kicker rotationAs an example in the next figure a state diagram ispresented for a simple attack manoeuvre.Driveto goalApproachballRepositionto ballKickkick conditionfail to driveball possessionloose ballloose ballball conditionFigure 7. Simple attack manoeuvre.In this case, the attack manoeuvre has four discretestates. Each one corresponds to a continuous controlmode. The attack is subdivided in four possible phases:approach ball, reposition to ball, drive to goal and kick.This hybrid automata (see [8],[9] and [10] for a formaltreatment on hybrid systems) uses game specific eventsrelated with the robot, ball and target goal, as triggers forthe discrete state jumps (more precisely the guardconditions detects these events and the reset relations arethe identity). The robot initially approaches the ball, andwhen has control of it, drives the ball towards the targetgoal. The first control law provides the approachingmovement and the second defines the angular velocity andorientation of the kicker in order to preserve ball controland drive it to target. In the event of losing the ball, therobot executes a different control mode to re-approach theball from a direction compatible with the target ball. If therobot has ball control and some kicking condition is met(minimum distance and good goal probability of success)it kicks the ball.Figure 7 - Robot photoIn the following figure overall control systemarchitecture is presented.HLow level motion controlVision SystemH H HHSensors boardVector2XIrodometryKicker SystemMotion SystemManoeuvre Control Game Status Identification Strategy CoordinatorSelflocalisationBalllocalisationOther robotslocalisationcompassFigure 9 – System architecture.7. Coordination StrategyThe team game evolution is coordinated in structuralway [2], [5], by defining the tactical function (goalkeeper; defence; middle field; attack) for each robot .Therobot adopts the corresponding tactical policy accordinglyto its perception about the companions in the field. Theplayer has one main place in the overall strategy (asconsequence of its tactical position), but this can bereconfigured dynamically. Only one robot has thepossibility to adopt the goal keeper position.The overall strategy solution results from thecomposition of the decisions taken in a distributed way bythe operational robots in the field. The analysis of thevectors: game phase, ball possession and current topologyformation in conjunction with the current robot game roleand its perceived topological position determines thecoordination level for each robot. An evaluation of thenext action to be taken is made by the maximisation ofhypothesis success.The coordination level is implemented by a modularand distributed controller synthesis trough thecomposition of discrete observers (corresponding to theanalysis vectors) and a discrete controller parameterisedby the adopted tactical functionalitypauseretireWaitStartGameonOffgamecontinueprepare startstart gamein gameready to start..SuspendedRestartingPositioningFigure 10 – Coordination high level automata.In the previous figure a high level automata for thevehicle coordination is presented. This automata depictsthe discrete game status. The robot can be either off game,or in several game and pre-game situations. These rangefrom the positioning phase in the beginning before game,the waiting to start (already positioned in the field) and ageneral playing phase. It can also be temporarilysuspended and return to game (for instance in asubstitution or repair). Each game phase can be itself acomplex hybrid automata with different discrete states.The hierarchical structure of the control architectureallows the modularity and incremental development of thesystem. In addition provides the execution of complexbehaviours and refinement in the overall coordinationstrategy.Figure 11 - Team photo8. Conclusions and Future WorkThe control and localisation for the robotic soccer teamISePorto design is presented.We have a full team plus a spare robot ready. The teamhas participated in several competitions namely in theGerman Open 2002 held in Paderborn, Germany andRobótica 2002 in Aveiro, Portugal.This complex mobile robotics is a motivating exampleto the research and development of control andnavigation. The control and navigation architecture for therobots is described.This hierarchic architecture entails the use of hybridcontrol automata in order to achieve complex behaviours.The coordination and game strategy issues are alsoreferred.The team status is still at an initial development stage,with a vast number of areas to be developed. In these wecould name the other players localisation, game statusobserver development, team topological formationclassifier, additional control manoeuvres and gamecontrol/API software development.AcknowledgementThe authors would like to thank the LSA team for theirefforts and dedication in developing the robots andsupport on the participation of competitions and events.References[1] J. M. Almeida, F. L. Pereira, J. B. Sousa, “A HybridFeedback Control System for a Nonholonomic Car-like Vehicle,” IEEE International Conference onRobotics and Automation, Albuquerque, New Mexico,1997.[2]  E. P.Silva, F. L. Pereira and J. B. Sousa, “On theDesign of a Control Architecture for an AutonomousMobile Robot,” chap. 15, in Advances In IntelligentAutonomous Systems, ed. S.G. Tzafestas, KluwerAcademic Publishers, 1999.[3] ] E. P. Silva, J. M. Almeida, A. Martins, et al, "Multi-task Real-Time environment for an autonomousmobile platform", PO-ROBOT Project Final Report,1999.[4] J. M. Almeida, E. P. Silva, A. Martins, et al,"Functional Layer of the control architecture PO-ROBOT Project Final Report, 1999.[5] E. P. Silva, F. L. Pereira, J. M. Almeida, "Coordinationand Organisation layer", PO-ROBOT Project FinalReport, 1999.[6] Covariance Intersection Working Group (CIWG), "ACulminating Advance in the Theory and Practice ofData Fusion, Filtering, and Decentralised Estimation".[7] A. Martins, J. Almeida, E. P. Silva and J. P. Baptista, "Design of the ISePorto Robocup Middle-Size LeagueRobotic Soccer Team: Control, Localisation andCoordination ,” Proc. MED2002  MediterraneanConference On Control and Automation, Lisbon, July ,2002.[8] J. Lygeros, C. Tomlin and S. Sastry, “Controllers forreachability specifications for hybrid systems,”,Automatica, Vol, 35, pp 349-370, 1999.[9] R. K. Boel, B. De Schutter et al, “Approaches toModelling, Analysis, and Control of Hybrid Systems,”, Technical Report BDS:99-04, Delft University ofTechnology, 1999.[10]Thomas Henzinger, “The Theory of HybridAutomata”, Technical Report UCB/ERL M96/28, U.C. Berkeley, 1996.

Control and Localisation for the ISePorto Robotic Soccer Team

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Control and Localisation for the ISePorto Robotic Soccer Team

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