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

Courtesy James McLurkin 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 The goal of the Swam project is to developing techniques for programming a large swarm of autonomous mobile robots.  Each robot is autonomous and there is no central controller.  Instead, group behaviors are produced from the local interactions of many individual robots.  A programming system for the Swarm would have to reverse this process; given a desired group behavior, what combination of local behaviors are required?  Our approach is to design primitive global behaviors that can be recombined to form more complex group programs. http://people.csail.mit.edu/jamesm/presentations/SwarmInANutshell/ http://people.csail.mit.edu/jamesm/presentations/SwarmInANutshell/http://people.csail.mit.edu/jamesm/presentations/SwarmInANutshell/swarm3%20disperse.mpg http://people.csail.mit.edu/jamesm/presentations/SwarmInANutshell/swarm3%20orbit.mpg Courtesy James McLurkin

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

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

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% k = 1 2 3 4 5 6 7 8 9 10 11 total_class_correct = 0.6250 0.6000 0.6500 0.5250 0.5500 in_class_correct = 0 0 6 1 0 0 5 3 10 0 0 6 1 0 0 5 4 8 0 0 6 1 0 0 5 4 10 0 0 6 1 0 0 0 4 10 0 0 6 1 0 0 0 4 11 0 0 6 1 0 0 1 4 10 tests_per_class = 0 0 6 8 0 0 6 8 12 NaN NaN 1.0000 0.1250 NaN NaN 0.8333 0.3750 0.8333 NaN NaN 1.0000 0.1250 NaN NaN 0.8333 0.5000 0.6667 NaN NaN 1.0000 0.1250 NaN NaN 0.8333 0.5000 0.8333 NaN NaN 1.0000 0.1250 NaN NaN 0 0.5000 0.8333 NaN NaN 1.0000 0.1250 NaN NaN 0 0.5000 0.9167 NaN NaN 1.0000 0.1250 NaN NaN 0.1667 0.5000 0.8333

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% 2 layer percentCorrect = Columns 1 through 12 0.3500 0.3500 0.3500 0.2500 0.5500 0.3000 0.6000 0.6000 0.4500 0.5000 0.3500 0.3000 Columns 13 through 21 0.6000 0.4500 0.2500 0.6500 0.4500 0.6500 0.4000 0.5500 0.2500

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 To ensure that a model of an environment is current, mobile robots must use information in the model to determine which regions in the environment from where to gather data. The data is, in turn, used to update the model. We refer to the process of continuously updating large-scale dynamic models of the physical world as world crawling. Database: 1) What is the object? 2) Where is the object relative to robot “me” 3) Where am I relative to home (or local origin)? 4) What time is it? 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

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 (feature vector changing similarly over time)

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