Robot Recognition of Robot Recognition of Complex Swarm Complex Swarm

BehaviorsBehaviorsAisha Walcott-MAS622J-Dec. 11, 2006Aisha Walcott-MAS622J-Dec. 11, 2006

IntroductionIntroduction

A Swarm is a large collection of autonomous mobile A Swarm is a large collection of autonomous mobile robotsrobots

No centralized controlNo centralized control

Group behaviors are produced from local interactions of Group behaviors are produced from local interactions of many individual robotsmany individual robots

Goal is to develop a suite of primitive global behaviors Goal is to develop a suite of primitive global behaviors that combine to form more complex group programsthat combine to form more complex group programs

Dispersion

Courtesy James McLurkinCourtesy James McLurkin

Orbit

Project GoalProject Goal

0 0.5 1 1.5 2 2.5 30

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DisperseDisperse OrbitOrbit ClusterCluster Bubble SortBubble Sort

source

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Example FeaturesExample Features hi source

low source

source

Build multi-classifiers to classify Complex Swarm BehaviorsBuild multi-classifiers to classify Complex Swarm Behaviors

ApproachApproach

Collect raw behavior data sets Collect raw behavior data sets

Determine Features (8D)Determine Features (8D)

Pattern Recognition AlgorithmsPattern Recognition Algorithms

KNNKNN

Neural NetsNeural Nets

Bayes NetsBayes Nets

Analyze results of each algorithmAnalyze results of each algorithm

KNNKNN

Tested a range of values for nearest Tested a range of values for nearest neighbors random tie breakneighbors random tie break

1 2 3 4 5 6 7 8 9 10 110.52

0.54

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0.58

0.6

0.62

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0.66

k neighbors

perc

ent

corr

ect

KNN for varying k on 4 Swarm robot behaviors

Overall Correct ClassificationOverall Correct Classification Average Class ClassificationAverage Class Classification

Cluster= 100%Cluster= 100%

Disperse = Disperse = 12.5%12.5%

Clump = 50%Clump = 50%

Orbit = 44%Orbit = 44%

Bubble Sort = Bubble Sort = 82%82%

Neural NetsNeural Nets

Single Hidden LayerSingle Hidden LayerLayer 1: nodes [50,70]Layer 1: nodes [50,70]Max percent = 65%Max percent = 65%LogsigLogsig

Two Hidden LayerTwo Hidden LayerLayer 1: nodes [50,70]Layer 1: nodes [50,70]Layer 2: nodes [25,25]Layer 2: nodes [25,25]Max percent = 65%Max percent = 65%

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0.55

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number of nodes neighbors

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2 Layer NN for with number of units = [50, 70] on 4 Swarm robot behaviors

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number of nodes

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1 Layer NN for with number of units = [50, 70] on 4 Swarm robot behaviors

Bayes NetsBayes Nets

Mapping to discrete domain by Mapping to discrete domain by applying k-means clustering to applying k-means clustering to each featureeach feature

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Cluster, Disperse,Clump,Orbit, Bubble SortCluster, Disperse,Clump,Orbit, Bubble Sort

22 88

Preliminary ResultsPreliminary ResultsClassification of ClusterClassification of Cluster

Possible bug in codePossible bug in code

Modify the discrete mappingModify the discrete mapping

DiscussionDiscussion

KNN and Neural Net performed wellKNN and Neural Net performed wellDetermining the mapping from real Determining the mapping from real

numbers to a discrete domain may numbers to a discrete domain may affect Bayes Nets classifiersaffect Bayes Nets classifiers

Overall high classification of clustering Overall high classification of clustering behaviorbehavior-Features tuned to behavior-Features tuned to behavior-Not enough variety of samples-Not enough variety of samples

Need more samples of varying Need more samples of varying behaviorbehavior

Next StepsNext Steps

Feature selection-which group of Feature selection-which group of features work best for each classifierfeatures work best for each classifier

Additional experiments to determine Additional experiments to determine why certain classifications are much why certain classifications are much betterbetter

FutureFuture

Use the temporal information to learn Use the temporal information to learn hidden emergent sub-behaviorshidden emergent sub-behaviors

Thank YouThank You