STP: Skills, tactics and plays for multi-robot control in adversarial environments

29 Pages·2004·0.36 MB·English

STP: Skills, tactics and plays for multi-robot control
in adversarial environments
BrettBrowning,JamesBruce,MichaelBowling,andManuelaVeloso
{brettb,jbruce,mhb,mmv}@cs.cmu.edu
CarnegieMellonUniversity,5000ForbesAvenue,PittsburghPA15213,USA
November22,2004
Abstract
Inanadversarialmulti-robottask,suchasplayingrobotsoccer,decisionsforteamandsinglerobot
behaviormustbemadequicklytotakeadvantageofshort-termfortuitouseventswhentheyoccur.When
nosuchopportunitiesexist,theteammustexecutesequencesofcoordinatedactionacrossteammembers
that increases the likelihood of future opportunities. We have developed a hierarchical architecture,
called STP, to control an autonomous team of robots operating in an adversarial environment. STP
consistsofSkillsforexecutingthelow-levelactionsthatmakeuprobotbehavior,Tacticsfordetermining
what skills to execute, and Plays for coordinating synchronized activity amongst team members. Our
STP architecture combines each of these components to achieve autonomous team control. Moreover,
theSTPhierarchyallowsforfastteamresponseinadversarialenvironmentswhilecarryingoutactions
withlongergoals. Inthisarticle,wepresentourSTParchitectureforcontrollinganautonomousrobot
team in a dynamic adversarial task that allows for coordinated team activity towards long-term goals,
with the ability to respond rapidly to dynamic events. Secondly, we present the sub-component of
skillsandtacticsasageneralized,single-robotcontrolhierarchyforhierarchicalproblemdecomposition
withflexiblecontrolpolicyimplementationandreuse. Thirdly, wecontributeourplaytechniquesasa
generalizedmethodforencodingandsynchronizingteambehavior,providingmultiplecompetingteam
responses,andforsupportingeffectivestrategyadaptationagainstopponentteams. STPhasbeenfully
implemented on a robot platform and thoroughly tested against a variety of unknown opponent teams
under in a number of RoboCup robot soccer competitions. We present these competition results as a
mechanismtoanalyzetheperformanceofSTPinarealsetting.
Keywords: Multi-robotcoordination,autonomousrobots,adaptivecoordination,adversarialtask
1 Introduction
To achieve high performance, autonomous multi-robot teams operating in dynamic, adversarial environ-
mentsmustaddressanumberofkeychallenges. Theteammustbeabletocoordinatetheactivitiesofeach
team member towards long-term goals, but also be able to respond in real-time to unexpected situations.
Here, real-time means responding at least as fast as the opponent. Moreover, the team needs to be able to
adaptitsresponsetotheactionsoftheopponent. Atanindividuallevel,therobotsmustbeabletoexecute
sequencesofcomplexactionsleadingtowardslong-termgoals,butalsorespondinreal-timetounexpected
situations. Secondly,eachrobotmusthaveasufficientlydiversebehaviorreportoireandbeabletoexecute
thesebehaviorsrobustlyeveninthepresenceofadversariessoastomakeagoodteamstrategyviable. Al-
though these contrasting demands are present in multi-robot [30, 17] and single-robot problems [9, 2, 32],
the presence of adversaries compounds the problem significantly. If these challenges are not addressed for
a robot team operating in a dynamic environment, the team performance will be degraded. For adversarial
environments,whereateam’sweaknessesareactivelyexploitedbygoodopponents,theteamperformance
willdegradesignificantly.
The sheer complexity of multi-robot teams in adversarial tasks, where the complexity is essentially
exponential in the number of robots, creates another significant challenge to the developer. Thus, control
policy reuse across similar sub-problems, as well as hierarchical problem decomposition, are necessary to
makeeffecientlyuseofdevelopertimeandresources.
Addressing all of these challenges in a coherent, seamless control architecture is an unsolved problem,
to date. In this paper, we present a novel architecture, called STP, for controlling a team of autonomous
robotsoperatinginatask-drivenadversarialenvironment. STPconsistsofthreemaincomponents–Skills,
Tactics, and Plays – built within a larger framework providing real-time perception and action generation
mechanisms. Skills encode low-level single-robot control algorithms for executing a complex behavior to
achieveashort-term,focusedobjective. Tacticsencapsulatewhattherobotshoulddo,intermsofexecuting
skills, to achieve a specific long-term goal. Plays encode how the team of robots should coordinate their
execution of tactics in order to achieve the team’s overall goals. We beleive that STP addresses many
of the challenges to multi-robot control in adversarial environments. Concretely, STP provides three key
contributions. Firstly, it is a flexible architecture for controlling a team of robots in a dynamic, adversarial
task that allows for both coordinated actions towards long-term goals, and fast response to unexpected
events. Secondly, the skills and tactics component can be decoupled from plays, and supports hierarchical
controlforindividualrobotsoperatingwithinadynamicteamtask,potentiallywithadversaries. Lastly,the
play-based team strategy provides a generalized mechanism for synchronizing team actions and providing
foradiversityofteambehavior. Additionally,playscanbeeffectivelyusedtoallowforstrategyadaptation
against opponent teams. STP has been fully implemented and extensively validated within the domain of
RoboCuprobotsoccer[23]. Inthispaper,wedetailthedevelopmentofSTPwithinthedomainofRoboCup
robot soccer, provide evidence of its performance in real competitions with other teams, and discuss how
ourtechniquesapplytomoregeneraladversarialmulti-robotproblems.
Thisarticleisstructuredasfollows. Inthefollowingsection,webeginbydescribingtheproblemdomain
ofRoboCuprobotsoccerwithinwhichSTPhasbeendeveloped. Section3presentsanoverviewoftheSTP
architecture and its key modules leading to a detailed description of the single robot components of skills
and tactics in section 4 and team components of plays in section 5. Section 6 describes the peformance of
STPinRoboCupcompetitionsagainstavarietyofunknownopponentteams,anddiscusseshowSTPcanbe
improvedandappliedtootheradversarialproblemdomains. Finally, section7presentsrelatedapproaches
toSTP,andsection8concludesthepaper.
2 The Robot Soccer Problem
TheSTParchitectureisapplicabletoanautonomousrobotteamperformingataskinanadversarial,dynamic
domain. Toconcretelyexplorethisproblem,wechoseRoboCuprobotsoccerasthetest-beddomain. More
specifically,wehavechosentheSmall-SizeLeague(SSL),adivisionwithintheRoboCupinitiative. Inthis
section,theSSLrobotsoccerproblemisconcretelydefinedalongwiththechallengesitposes. Thissection
alsodetailsthespecifictest-bed,theCMDragonssystem,usedtovalidatetheSTParchitecturetoprovidea
backdropfortheensuingsections.
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2.1 Small-sizeRoboCupRobotSoccerLeague
RoboCuprobotsoccerisaworld-wideinitiativedesignedtoadvancethestate-of-the-artinrobotintelligence
through friendly competition, with the eventual goal of achieving human-level playing performance by
2050 [23]. RoboCup consists primarily of teams of autonomous robots competing against one another
in games of soccer, along with an associated symposium for research discussion. There are a number of
different leagues within RoboCup, which are designed to focus on different parts of the overall problem:
developingintelligentrobotteams. ThisarticleisprimarilyfocusedontheSmall-SizeLeague(SSL).
A SSL game consists of two teams of five robots play soccer on a 2.8m x 2.3m field with an orange
golfball[3]. Eachteammustbecompletelyautonomousforthedurationofthegame,whichtypicallylasts
fortwo10-minutehalves. Here,autonomymeansthattherearenohumansinvolvedinthedecisionmaking
cyclewhilethegameisinprogress. TheteamsmustobeyFIFA-likerulesasdictatedbyahumanreferee. An
assistant referee translates referee commands into a computer-usable format, which is transmitted to each
team via RS-232 using a standardized protocol, via a computer running the RefBox program [3]. Figure 1
showsthegeneralsetupasusedbymanyteamsintheSSL.TheSSLisdesignedtofocusonteamautonomy.
Therefore, global vision via overhead cameras and off-field computers, which can communicate with the
robotsviawirelessradio,areallowedtobeused.
Figure1: AnoverviewoftheCMDragonssmall-sizerobotsoccerteam.
SSLrobotsoccerinvolvesmanyresearchissues. Examplesofsomeoftheresearchchallengesinclude:
• Buildingcomplete,autonomouscontrolsystemsforadynamictaskwithhigh-performance;
• Teamcontrolinadynamicenvironment,andresponsetoanunknownopponentteam;
• Behaviorgenerationgivenrealsensorlimitationsofocclusion,uncertainty,andlatency;
• Fastnavigationandballmanipulationinadynamicenvironmentarereal-worldsensors;
• Fast,robust,low-latencyvision,witheasytousecalibrationroutines;
• Robust,highperformancerobotswithspecializedmechanismsforballmanipulation.
A typical SSL game is highly dynamic, where ball speeds of 3 to 4m.s−1 and robots speeds of 1 to
2m.s−1 are common. With such speeds in a small environment, it becomes critical for information to be
translated into action quickly in order for the team to be responsive to sudden events in the world. For
example, ifa robotkicks aball at 3.5m.s−1, alatency of 100ms meansthat theball willhave movedover
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35cmbeforetherobotscouldpossiblyrespondtotheobservationthattheballhadbeenkicked. Highspeed
ofmotionandlatencyimpactoncontrolinthefollowingways:
• Vision, tracking, and modeling algorithms must compromise between the need to filter noise and
detectunexpectedeventsinminimumtime;
• Predictionmechanismsarerequiredtocompensateforlatencyforeffectivecontrol;
• Teamandsinglerobotcontrolmustadaptquicklytodynamicchanges.
The last point means that all control decisions need to be recalculated as often as possible to allow the
system to react quickly to unexpected events. As a rough guide, the CMDragons system [12] recalculates
everything for each frame, at a rate of 30Hz. Typically, high-level decisions change at a slower rate than
low-leveldecisions. Foranapproximateguide,aplaytypicallylasts5-30s,whileatacticmayoperateover
a time frame of 1-30s, and a skill may operate over a 300ms-5s time frame. However, any decision at
any level can be switched in the minimum time of one frame period (33ms) to respond to any large scale
dynamicchange.
2.2 TheCMDragons
Figure2showsthemajorcomponentsofthecontrolsystemdevelopedforourCMDragonsSSLteam. This
architectureistheresultofalongseriesofdevelopmentssinceRoboCup1997[37,36,35,10,12]. Figure3
shows the robot team members. As shown, the architecture consists of a number of modules beginning
withvisionandtracking, theSTParchitecture,navigationandmotioncontrol,andfinallytherobotcontrol
software and hardware. We briefly describe each of the non-STP components in the following paragraphs
toprovidethecontextforlaterdiscussions.
Information passes through the entire system synchronized with incoming camera frames at 30Hz.
Thus a new frame arrives, vision and tracking are run on the processed frame, the resulting information is
fed into the world model. The STP architecture is executed, followed by navigation and motion control.
The resulting motion command is sent to the robot and the robot executes the command with local control
routines.
2.2.1 Perception
Vision is the primary means of perception for the CMDragons team. Everything in the SSL is color coded
(seeFigure3),makingcolorvisionprocessingalgorithmsanaturalchoice. Theballisorangeandthefield
isgreencarpetwithwhitelinesandwhiteangledwalls. Eachrobotispredominantlyblackwithayellowor
bluecircularmarkerinitscenter. Dependinguponwhowinsthetossofthecoinbeforethegame,oneteam
usesyellowmarkerswhiletheotherusesblue. Eachrobottypicallyhasanothersetofmarkersarrangedin
some geometric pattern that uniquely identifies the robot and its orientation. Knowledge of an opponents
additionalmarkersisusuallynotavailablebeforeagame.
In the CMDragons team, images from the camera arrive at a frame rate of 30Hz into an off-field com-
puter. Forreferencepurposes,mostofthesystemdescribedhererunsona2.1GHzAMDAthlonXP2700+
system, although a 1.3GHz processor was used previously without any difficulties. Using our fast color
visionlibrary,CMVision[11],coloredblobsareextractedfromeachimage. Thecolorsareidentifiedbased
onpriorcalibrationtoproduceathresholdmappingfrompixelvaluestosymboliccolor. Withknowledgeof
eachrobotsuniquemarkerlayout,high-levelvisionfindseachrobotintheimageanddeterminesitsposition
and orientation. The position of the ball and each opponent robot is also found. Orientation for opponents
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Figure2: OverviewoftheCMDragonsteamarchitecture.
cannot be found owing to the lack of advance knowledge on their marker layout. The world position of
each object is then determined via a parametric camera model learned during game setup. Full details on
thevisionalgorithmscanbefoundin[14].
FilteredpositionandvelocityinformationisderivedusingasetofindependentExtendedKalman-Bucy
Filters (EKBF) for each object (see [12]). As velocity information cannot be derived from each camera
imagealone,andthereistoomuchnoiseforframedifferentialstobeeffective,theEKBF’sareusedtoboth
nullifytheeffectsofnoiseandintermittency(missingdata). Additionally,theEKBF’sprovideaprediction
mechanism through forwardmodeling which is useful forovercoming latency. Insummary, the fullvision
andtrackingmoduleprovidesestimatesofeachrobotlocationandorientation,eachopponentlocation,and
balllocation,withvelocitiesforallelevenobjects. Takentogether,theseestimatesprovidetherobot’sbelief
stateaboutthestateoftheworld.
2.2.2 WorldModelBeliefState
Allbeliefsaboutthestateoftheworld,wheretherobotsareetc.,areencapsulatedinaworldbeliefmodel.
In short, the world model acts as a centralized storage mechanism for beliefs for all layers of the control
architecturetouse. Thebeliefmodelcontains:
• Allperceptualinformationobtainedfromthetracker(e.g.,robotpositionsandvelocities);
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Figure 3: The CMDragons robots. The robot on the left is an OmniBot, while the robots on the right are
DiffBots. Eachrobotfitswithinan18cmdiametercylinderthatis15cmtall.
Figure4: TheCMDragonscontrolarchitectureisbasedonway-pointprimitives.
• Gamestateinformationderivedfromthereceivedrefereecommands;
• Opponentmodelinginformationderivedfromstatisticalmodelsofobservedopponentbehavior;
• High-levelpredicatesderivedfromtheperceivedstate,suchaswhichteamhaspossessionoftheball,
isinattack,isinaparticularroleetc.
Each high-level predicate, is a boolean function of the the tracker and/or game state belief. To account
fornoise,eachbooleanfunctionincorporatesempiricallydeterminedhysteresistopreventundueoscillation
at the decision boundary. These predicates, due to their boolean nature, provide a symbolic representation
that is often more useful for making decisions than the raw belief models. For example, deciding whether
torunanattackingplayoradefensiveplay.
2.2.3 NavigationandMotionControlActionInterface
TheSTParchitectureconsistsofteamcontrolandindividualrobotcontrol. Followingthecommontechnique
of hybrid hierarchical control [19, 33], we have developed lower modules for obstacle-free navigation and
motioncontrol. Essentially,thesemodulesprovideresourcestotherobotforgeneratingactionsintheworld.
The resources provide are obstacle-free navigation, motion control, and direct robot commands. Figure 4
showsthecontrolhierarchy.
Thenavigationmodulegeneratesanear-optimal,obstaclefreepathtothegoallocationusingthebeliefs
storedintheworldmodel. Basedonthispath,themotioncontrolmodulecalculatesatrajectorytoachievea
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short-termtargetway-pointonthepaththatdoesnotcollidewithobstacles. Usingthistrajectory,avelocity
commandisissuedtotherobothardwaretoexecute.
Due to the dynamic nature of robot soccer, both navigation and motion control are recalculated each
frame,foreachrobot. Thisplacesstrictcomputationallimitationsoneachofthesemodules. Wehavedevel-
opedandimplementedafast,randomizedpathplanner[13]basedontheRapidly-exploringRandomTrees
(RRT’s) algorithm [24]. Similarly, we have developed a trapezoidal-based, near-optimal motion control
algorithmforquicklygeneratingrobotmotioncommands[12].
2.2.4 RobotHardware
Each robot is an omni-directional platform capable of spinning while driving in any direction. Each robot
is equipped with a ball manipulation device that includes a solenoid actuated ’kicker’ and a motorized
’dribbler’. The kicker moves an aluminium plate to contact with the ball, propelling it at speeds of around
3.5−4m.s−1. Thedribblerisarubbercoatedbarthatismountedhorizontallyatballheightandconnectedto
amotor. Asthebarspinsagainstaball,itcausestheballtospinbackwardsagainsttherobottherebyallowing
the robot to move around effectively with the ball. Each robot has an on-board processor, and runs local
velocity-based servo loops using integrated encoder feedback and standard PID control techniques [29].
Additionally,therobotisequippedwithanFMradioreceiverwhichitusestoreceivemovementcommands
fromtheexternalcomputer.
3 The STP Architecture
ThissectionoverviewstheSTParchitectureleadingintoadetaileddiscussionofskills,tactics,andplays.
3.1 Goals
The presence of an opponent has many, sometimes subtle, effects on all levels and aspects of control.
Generating robust behavior that responds to the actions of the opponent is a significant challenge. The
challengesforteamcontrolare:
1. Execute a temporally extended sequence of coordinated activities amongst team members towards
somelongertermgoalwhilesimultaneouslyrespondingasateamtounexpectedeventsbothfortuitous
anddisastrousones.
2. Theabilitytorespondasateamtothecapabilities,tactics,andstrategiesoftheopponent.
3. Executerobustbehaviordespitesensorlimitationsandworlddynamics.
4. Provideamodular,compactarchitecturewithfacilitiesforeasilyconfiguringteamplay,andforana-
lyzingtheperformanceofthedecisionmakingprocess.
Thefirstandsecondgoalsaredirectimpactsfromcontrollingateamofrobotsinanadversarialenviron-
ment. Wedesiretheteamcontrolarchitecturetogeneraterobustbehaviorthatincreasesthechanceoffuture
opportunities against the opponent. Whenever such opportunities arise, whatever the cause, the team must
takeadvantageofthisopportunityimmediately. Conversely,ifanopportunityarisesfortheopponentteam,
our team must respond quickly and intelligently to minimize the damage the opponent can cause. Such
responsive behavior must occur throughout the architecture. Building responsive team while overcoming
the usual limitations of real world sensors, such as latency, noise, and uncertainty, is the major goal of the
STPframework.
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In robot soccer, robust development is a significant issue. Many teams have gone through bad experi-
encescausedbypoordevelopmentproceduresorfacilities. Thus,agoodarchitectureisonethatiscompact
and modular such that changes in one module have a minimal impact on the operation of another mod-
ule. Giventhenumberofparametersinacomplexteamarchitecture,theabilitytoeasilyreconfigurethose
parametersandtoanalyzetheperformanceofdifferentparametersettingsisextremelyusefultothedevel-
opmentcycle.
3.2 Skills,TacticsandPlays
Toachievethegoalsofresponsive, adversarialteamcontrol, wehavedevelopedtheSTParchitecture. The
key component of STP is the division between single robot behavior and team behavior. In short, team
behavior results from executing a coordinated sequence of single robot behaviors for each team member.
Wenowdefineplays,tactics,andskills,andhowtheyinteractforateamofN robots.
Aplay,P,isafixedteamplanwhichconsistsofasetofapplicabilityconditions,terminationconditions,
and N roles, one for each team member. Each role defines a sequence of tactics T1,T2... and associated
parameters to be performed by that role in the ordered sequence. Assignment of roles to team members is
performed dynamically at run time. Upon role assignment, each robot i is assigned its tactic T to execute
i
from the current step of the sequence for that role. Tactics, therefore, form the action primitives for plays
to influence the world. The full set of tactics can be partitioned into active tactics and non-active tactics.
Active tactics are those involved with ball manipulation. There is only one active tactic amongst the roles
perstepinthesequence. Thesuccessfulcompletionoftheactivetacticisusedtotriggerthetransitiontothe
nextstepinthesequenceforallrolesintheplay. Playsarediscussedingreaterdetailinsection5.
A tactic, T, encapsulates a single robot behavior. Each robot i executes its own tactic as created by
the current play P. A tactic T determines the skill state machine SSM to be executed by the robot i. If
i i
the tactic is an active one, it also contains evaluation routines to determine if the tactic has completed. If
the skill state machine differs from that executed previously, then execution begins at the first skill in the
state machine i.e. S . If the skill state machine did not change, then execution continues at the last skill
i
transitioned to. The tactic T also sets parameters SParams to be used by the executing skill S . Thus,
i i i
skillsformtheactionprimitivesfortactics.
A skill, S, is a focused control policy for performing some complex action. Each skill is a member
of one, or more, skill state machines SSM ,SSM ,.... Each skill S determines what skill it transitions
1 2
to S0 based upon the world state, the time skill S has been executing for, and the executing tactic for that
robot. The executing tactics may reset, or change and reset, the executing skill state machine. Each skill
can command the robot to perform actions either directly, through motion control, or through navigation.
If commanded through navigation, navigation will generate an intermediate, obstacle free way-point for
motioncontrolwhichwillthengenerateacommandtosendtotherobot.
Bothskillsandtacticsmustevaluatetheworldstate,insometimescomplexways,tomakeusefuldeci-
sions. Forexample,sometacticsdeterminethebestpositiontomovetoinordertoreceiveapass. Alterna-
tively,somedefensivetacticsevaluatewhichopponentrobotmightmovetoreceiveapassandwheretogo
topreventtheopponentachievingthisgoal. Topreventunnecessaryduplication,andtogreatermodularize
thearchitecture,weextracttheseevaluationsintoanevaluationmodulewhichisusablebybothtacticsand
skills. Tactics,skills,evaluationsaredetailedinsection4.
Plays, tactics, and skills, form a hierarchy for team control. Plays control the team behavior through
tactics, while tactics encapsulate individual robot behavior and instantiate actions through sequences of
skills. Skillsimplementthefocusedcontrolpolicyforactuallygeneratingusefulactions. Table1showsthe
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main execution algorithm for the STP architecture. The clear hierarchical arrangement of plays for team
control,tacticsforsinglerobotbehavior,andskillsforfocusedcontrolareshown.
ProcessSTPExecution
1. CaptureSensors()
2. RunPerception()
3. UpdateWorldModel()
4. P ←ExecutePlayEngine()
5. foreachroboti∈{1,...,N}
6. (T ,TParams )←GetTactic(P,i)
i i
7. (SSM ,SParams )←ExecuteTactic(T ,TParams )
i i i i
8. ifNewTactic(T )then
i
9. S ←SSM (0)
i i
10. (command ,S0)←ExecuteStateMachine(SSM ,S ,SParams )
i i i i i
11. robot command ←ExecuteRobotControl(command )
i i
12. SendCommand(i,robot command )
i
Table1: ThemainSTPexecutionalgorithm.
4 Tactics and Skills for Single Robot Control
Single robot control in the STP architecture consists of tactics and skills. Tactics provide the interface for
teamcontrolviaplays,whileskillsprovidethemechanismsforgeneratingbehaviorinacompact,reusable
way. Webeginbydescribingtacticsingreaterdepth,followedbyskills,andfinallytheevaluationmodule.
4.1 Tactics
Tacticsarethetopmostlevelofsinglerobotcontrol. Eachtacticencapsulatesasinglerobotbehavior. Each
tacticisparameterizedallowingformoregeneraltacticstobecreatedwhichareapplicabletoawiderrange
ofworldstates. Throughparameterizationawiderrangeofbehaviorcanbeexhibitedthroughasmallerset
of tactics, making play design easier. Table 2 provides the list of tactics we have implemented for robot
soccer. Themeaningofeachtacticshouldbereasonablyobviousfromthetacticname.
During execution, one tactic is instantiated per robot. A tactic, as determined by the executing play,
is created with the parameters defined for the play. That tactic then continues to execute until the play
transitionstothenexttacticinthesequence. Asdescribedabove,eachtacticinstantiatesactionthroughthe
skilllayer. Inshort,thetacticsdeterminewhichskillstatemachinewillbeused,andsetstheparametersfor
executingthoseskills. Exampleparametersincludetargetway-points,targetpointstoshootat,opponentsto
mark,andsoon. Differenttasksmayusemanyofthesameskills,butprovidedifferentparameterstoachieve
the different goals of the tactic. The shooting and passing tactics are good examples. The skills executed
by the two are very similar, but the resulting behavior can be quite different due to the different parameter
assignments. Finally,eachtacticmaystoreanylocalstateinformationitrequirestoexecuteappropriately.
Table3showsthealgorithmfortheshoottacticusedtokicktheballatthegoalortowardsteammates
forone-shotdeflectionsongoal. Notshownaretheconditioningofthetacticdecisiontreeontheparameters
specifiedbytheactiveplay. Inthiscase, theplaycanonlydisabledeflectiondecisions. Thetacticconsists
of evaluating the options of shooting directly on goal, or shooting to a teammate to deflect or kick on goal
inaso-calledone-shotpass. Eachoptionisassignedascorewhich,loosely,definesalikelihoodofsuccess.
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Active Tactics
shoot (Aim | Noaim | Deflect hrolei)
steal [hcoordinatei]
clear
active def [hcoordinatei]
pass hrolei
dribble to shoot hregioni
dribble to region hregioni
spin to region hregioni
receive pass
receive deflection
dribble to position hcoordinatei hthetai
position for start hcoordinatei hthetai
position for kick
position for penalty
charge ball
Non-Active Tactics
position for loose ball hregioni
position for rebound hregioni
position for pass hregioni
position for deflection hregioni
defend line hcoordinate-1i hcoordinate-2i hmin-disti hmax-disti
defend point hcoordinate-1i hmin-disti hmax-disti
defend lane hcoordinate-1i hcoordinate-2i
block hmin-disti hmax-disti hside-prefi
mark horolei (ball | our goal | their goal | shot)
goalie
stop
velocity hvxi hvyi hvthetai
position hcoordinatei hthetai
Table2: Listoftacticswiththeiracceptedparameters.
Much of the operation of determining the angles to shoot at and generating the score is pushed into the
evaluationmodule,describedinsection4.3.
The tactic, indeed nearly all tactics, make use of additive hysteresis in the decision making process.
Hysteresis is a necessary mechanism to prevent debilitating oscillations in the selected choice from frame
toframe. Eachactionintheshoottactic,aswithanyothertactic,takesanon-negligibleperiodoftimeto
performthatissubstantiallygreaterthanasingledecisioncycleat30Hz. Withthedynamicsoftheenviron-
mentfurthercomplicatedbyocclusion,noise,anduncertainty,itsisoftenthecasethattwoormorechoices
willoscillateovertimeintermsofitsscore. Withouthysteresis,therewillbecorrespondingoscillationsin
theactionchosen. Theendresultisoftenthattherobotwilloscillatebetweendistinctlydifferentactionsand
effectively be rendered immobile. The physical manifestation of this behavior, ironically, is that the robot
appearsto’twitch’andbe’indecisive’. Inmostrobotdomains,suchoscillationswilldegradeperformance.
Inadversarialdomainslikerobotsoccer,whereitisimportanttocarryoutanactionbeforetheopponentcan
respond,suchoscillatescompletelydestroy. Hysteresisprovidesausable,easilyunderstandable,mechanism
forpreventingsuchoscillationsandisusedpervasivelythroughouttheSTParchitecture.
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