Robot Recognition of Complex Swarm Behaviors

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Robot Recognition of Complex Swarm Behaviors
Aisha Walcott-MAS622J-Dec. 11, 2006
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Introduction
Dispersion
Orbit

• A Swarm is a large collection of autonomous
  mobile robots
• No centralized control
• Group behaviors are produced from local
  interactions of many individual robots
• Goal is to develop a suite of primitive global
  behaviors that combine to form more complex group
  programs

Courtesy James McLurkin
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Project Goal
Build multi-classifiers to classify Complex Swarm
Behaviors
Disperse
Orbit
Cluster
Bubble Sort
Example Features
hi source
low source
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Approach

• Collect raw behavior data sets
• Determine Features (8D)
• Pattern Recognition Algorithms
• KNN
• Neural Nets
• Bayes Nets
• Analyze results of each algorithm

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KNN

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

Overall Correct Classification
Average Class Classification
Cluster 100 Disperse 12.5 Clump 50 Orbit
44 Bubble Sort 82
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Neural Nets

• Single Hidden Layer
• Layer 1 nodes 50,70
• Max percent 65
• Logsig

• Two Hidden Layer
• Layer 1 nodes 50,70
• Layer 2 nodes 25,25
• Max percent 65

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Bayes Nets

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

Preliminary Results Classification of
Cluster Possible bug in code Modify the discrete
mapping
Cluster, Disperse,Clump,Orbit, Bubble Sort
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Discussion

• KNN and Neural Net performed well
• Determining the mapping from real numbers to a
  discrete domain may affect Bayes Nets classifiers
• Overall high classification of clustering
  behavior
• -Features tuned to behavior
• -Not enough variety of samples
• Need more samples of varying behavior

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Next Steps

• Feature selection-which group of features work
  best for each classifier
• Additional experiments to determine why certain
  classifications are much better
• Future
• Use the temporal information to learn hidden
  emergent sub-behaviors

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Thank You