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Robot Recognition of Complex Swarm Behaviors

Aisha Walcott-MAS622J-Dec. 11, 2006. Robot Recognition of Complex Swarm Behaviors. Introduction. A Swarm is a large collection of autonomous mobile robots No centralized control Group behaviors are produced from local interactions of many individual robots

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Robot Recognition of Complex Swarm Behaviors

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  1. Aisha Walcott-MAS622J-Dec. 11, 2006 Robot Recognition of Complex Swarm Behaviors

  2. Introduction 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 Dispersion Orbit Courtesy James McLurkin

  3. source * source Project Goal Build multi-classifiers to classify Complex Swarm Behaviors Disperse Orbit Cluster Bubble Sort Example Features hi source low source

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

  5. 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%

  6. 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% Neural Nets

  7. 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 8 1 2

  8. 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

  9. 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

  10. Thank You

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