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Many physical and algorithmic swarms utilize inter-agent communication to achieve advanced swarming behaviors. These swarms are inspired by biological swarms that can be seen throughout nature and include bee swarms, ant colonies, fish schools, and bird flocks. These biological swarms do not utilize inter-agent communication like their physical and algorithmic counterparts. Instead, organisms in nature rely on a local awareness of other swarm members that facilitates proper swarm motion and behavior. This research aims to pursue an effective swarm algorithm using only line-of-sight proximity information and no inter-agent communication. It is expected that the swarm performance will be lower than that of a swarm utilizing inter-agent communication. Various sensors were studied and considered for this project but infrared sensors were ultimately selected. These sensors were then modeled in software using a neural network in order to calculate the minimum number of infrared transmitters and receivers necessary for each agent while still ensuring the proper functionality of the swarm. A physical swarm was then designed and constructed to observe the resulting swarm performance.

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Daniel Standish received his Master of Science in Computer Science with a concentration in Robotics in May of 2013 from the University of South Florida.At the time of this publication in 2016, he currently works as a software developer for a large government contractor in the Washington D.C. metropolitan area.

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