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October 19, Probabilistic Modeling III
- 6. Example:âŠcoverageâŠ
â˘âŻ AlgorithmâŠ
â⯠BuildâŠaâŠminimalâŠ
spanningâtreeâŠonâlineâŠ
â⯠MoveâŠfromâŠbladeâŠtoâŠ
bladeâŠreac$velyâŠ
â⯠Localiza$onâŠbyâŠcoun$ngâŠ
bladesâŠ
â⯠StartâoverâŠwhenâŠlostâŠ
â˘âŻ UncertaintyâŠ
â⯠Naviga$onâŠ
- 8. Quan$fyingâŠSensorâŠ&âŠActuatorâŠ
NoiseâŠ
6000âŠexperimentsâŠinâŠWebots,âŠ10%âŠwheelâslipâŠ
TimeâŠforâŠcoveringâŠoneâŠblade⊠ProbabilityâŠofâŠnoâŠnaviga$onâŠerrorâŠ
(geometricâŠdistribu$on)âŠ
- 9. DiscreteâŠEventâŠSystemâŠSimula$onâŠ
WebotsâGeneratedâŠâŠ
EventâŠTimeâŠDataâŠ
ChooseâŠrobotâŠ(closestâŠ
nextâŠeventâŠ$me),âŠaddâŠ
eventâŠ$meâŠforâŠrobotâŠ
DetermineâŠnextâŠnodeâŠnâŠtoâŠvisit⊠AlgorithmâŠ
Naviga$on⊠FailureâŠ
Success?⊠probabilitesâŠ
Yes⊠NoâŠ
MoveâŠRobot⊠MoveâŠRobot⊠NoâŠ
toâŠn⊠somewhereâŠelseâŠ
AllâŠBladesâŠ
inspected?⊠YesâŠ
- 15. Robo$câŠPlaeormâŠ
Localiza(on⊠VisionâŠ
HagisonicâŠStargazer⊠LogitechâŠQuickCamâŠ
Computa(onâŠ
DellâŠLa$tudeâŠD620âŠ
Manipula(onâŠ
CrustcrawlerâŠ4âDOFâŠ
WateringâŠSystemâŠ
HargraveâŠ
DiďŹeren(alâŠWheelsâŠ
iRobotâŠCreateâŠ
UbuntuâŠLinux,âŠWillowâŠGarageâŠROS,âŠUSBâŠ
- 16. PlantâŠ
HumidityâŠSensorâŠ
VegetronixâŠ
WirelessâŠrouterâŠ
Temperature@lertâŠ
InfraâredâŠBeaconâŠ
iRobotâŠRoombaâŠbase⊠OpenWRTâŠLinux,âŠAtherosâŠchipsetsâŠ
- 19. InventoryâŠ
â˘âŻ ChallengesâŠ
â⯠Percep$on⊠1⊠6âŠ
â⯠NotâŠpossibleâŠfromâŠsingleâŠperspec$veâŠ
â˘âŻ Algorithm⊠2⊠5âŠ
-⯠FetchâŠfruitâŠinventoryâŠfromâŠpotâŠ(JSON)âŠ
-⯠ObjectâŠrecogni$onâŠfromâŠ6âŠnonâ 3⊠4âŠ
overlappingâŠperspec$vesâŠ
-⯠MergeâŠobserva$onâŠwithâŠinventoryâŠ
â˘âŻ ConďŹdenceâŠgrowsâŠwithâŠeveryâŠ
measurementâŠ
â˘âŻ InventoryâŠdura$on:âŠ45sâŠ
- 20. VisualâŠServoing/GraspingâŠ
â˘âŻ ChallengesâŠ
â⯠Percep$onâŠ(fruitsâŠ+âŠstem)⊠2âŠ
â⯠LimitedâŠDOFâŠ/âŠworkspaceâŠ
â˘âŻ AlgorithmâŠ
-⯠SelectâŠfruitâŠwithâŠtheâŠ
strongestâŠconďŹdenceâŠ
-⯠ServoâŠtoâŠini$alâŠposi$onâŠ
-⯠ServoâŠtoâŠfruitâŠusingâŠimageâŠ
JacobianâŠ
-⯠RelyâŠonâŠradiusâŠes$mateâŠforâŠ
depth⊠F.âŠChaumeleâŠandâŠS.âŠHutchinson,âŠâVisualâŠservoâŠcontrolâŠ
partâŠi:âŠBasicâŠapproaches,ââŠRobo$csâŠ&âŠAutoma$onâŠ
-⯠CloseâŠgripperâŠ/âŠretractâŠarm⊠Magazine,âŠvol.âŠ13,âŠno.âŠ4,âŠpp.âŠ82â90âŠ
whenâŠarrivedâŠ
- 22. TaskâŠAlloca$onâŠ
â˘âŻ ChallengesâŠ
â⯠UnreliableâŠchannelâŠ(adâ
hocâŠwiďŹ)âŠ
â⯠UncertaintyâŠinâŠ
naviga$onâŠandâŠtaskâŠ
execu$onâŠ
â˘âŻ RobotsâŠreplyâŠwithâŠtheirâŠ
distanceâŠ+âŠlengthâŠofâŠtaskâŠ
queueâŠ(approx.âŠ$me)âŠ
â˘âŻ PlantâŠselectsâŠâbestââŠ
robotâŠ
â˘âŻ Alloca$onâŠrepeatedâŠ
periodicallyâŠ
- 23. Naviga$onâŠ
â˘âŻ ChallengesâŠ
â⯠NarrowâŠpassagesâŠ
â⯠DeadlocksâŠ(mul$ârobot)âŠ
â⯠Communica$onâŠ
â˘âŻ Localiza$onâŠ
â⯠SensorâŠfusion:âŠodometryâŠ+âŠpassiveâŠ
infraredâŠbeaconsâŠ
â⯠BroadcastâŠposi$onâŠatâŠ1HzâŠ
â˘âŻ Mo$onâŠplanningâŠ
â⯠GridâmapâŠofâŠtheâŠenvironment:âŠsta$câŠ
obstaclesâŠ+âŠotherâŠrobotsâŠ
â⯠WavefrontâŠalgorithmâŠ(Latombe)âŠ
â⯠Reac$veâŠbehaviorâŠforâŠavoidingâŠ
bumpsâŠ
â⯠Reac$veâŠbehaviorâŠforâŠdockingâŠ
- 25. PossibleâŠmodelâŠ
T1:âŠHarvest⊠T2:âŠRobot⊠âŠxâŠ*âŠâŠ T3:âŠRobotâŠ
request⊠receivesâŠtask⊠73sâŠ+/ââŠ15s⊠reachesâŠplantâŠ
(p|Naviga$onâŠfailure)xâŠ
28.3s+/â10sâŠ
25%âŠ
Assump$onsâŠ
ââŻNoâŠtaskâŠalloca$onâŠ(singleâŠrobot)⊠T4:âŠRobotâŠ
ââŻInďŹniteâŠnumberâŠofâŠgraspingâŠtrial⊠graspsâŠ
NextâŠstepâŠ
âsimulateâŠtaskâŠalloca$onâŠbasedâŠonâŠ
communica$onâŠmodelâŠ
âďŹniteâŠnumberâŠofâŠfruitsâŠperâŠplantâŠ