Kagan Tumer's Publications

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Evolving Large Scale UAV Communication Systems. A. Agogino, C. Holmes Parker, and K. Tumer. In Proceedings of the Genetic and Evolutionary Computation Conference, Philadelphia, PA, July 2012. Best "Real World Applications" paper award.

Abstract

Unmanned Aerial Vehicles (UAVs) have traditionally been used for short duration missions involving surveillance or military operations. Advances in batteries, photovoltaics and electric motors though, will soon allow large numbers of small, cheap, solar powered unmanned aerial vehicles (UAVs) to fly long term missions at high altitudes. This will revolutionize the way UAVs are used, allowing them to form vast communication networks. However, to make effective use of thousands (and perhaps millions) of UAVs owned by numerous disparate institutions, intelligent and robust coordination algorithms are needed, as this domain introduces unique congestion and signal-to-noise issues. In this paper, we present a solution based on evolutionary algorithms to a specific ad-hoc communication problem, where UAVs communicate to ground-based customers over a single wide-spectrum communication channel. To maximize their bandwidth, UAVs need to optimally control their output power levels and orientation. Experimental results show that UAVs using evolutionary algorithms in combination with appropriately shaped evaluation functions can form a robust communication network and perform 180\% better than a fixed baseline algorithm as well as 90\% better than a basic evolutionary algorithm.

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BibTeX Entry

@inproceedings{tumer-holmesparker-uav_gecco12,
        author = {A. Agogino and C. Holmes Parker and K. Tumer},
        title = {Evolving Large Scale UAV Communication Systems},
        booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference},
	month = {July},
	address = {Philadelphia, PA},
	abstract={Unmanned Aerial Vehicles (UAVs) have traditionally been used for short duration missions involving surveillance or military operations.  Advances in batteries, photovoltaics and electric motors though, will soon allow large numbers of small, cheap, solar powered unmanned aerial vehicles (UAVs) to fly long term missions at high altitudes. This will revolutionize the way UAVs are used, allowing them to form vast communication networks. However, to make effective use of thousands (and perhaps millions) of UAVs owned by numerous disparate institutions, intelligent and robust coordination algorithms are needed, as this domain introduces unique congestion and signal-to-noise issues. In this paper, we present a solution based on evolutionary algorithms to a specific ad-hoc communication problem, where UAVs communicate to ground-based customers over a single wide-spectrum communication channel. To maximize their bandwidth, UAVs need to optimally control their output power levels and orientation. Experimental results show that UAVs using evolutionary algorithms in combination with appropriately shaped evaluation functions can form a robust communication network and perform 180\% better than a fixed baseline algorithm as well as 90\% better than a basic evolutionary algorithm.},
	bib2html_pubtype = {Award Winners,Refereed Conference Papers},
	bib2html_rescat = {Evolutionary Algorithms},
	note = {{\bf <em>Best "Real World Applications" paper award.</em>}},
        year = {2012}
        }

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