GENCEM: A genetic algorithms approach to coordinated exploration and mapping with multiple autonomous robots

Chris C. Sotzing, Win Mar Htay, Clare Bates Congdon

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

GENCEM is a genetic algorithms approach to coordinated exploration and mapping with multiple autonomous robots. Building on previous work in coordinated mapping, the work reported here compares static to evolutionary approaches for the same coordination tasks. In GENCEM, parameters affecting the coordination behaviors are evolved, leading to a decided improvement over hand-coded parameter settings across a variety of environments and using different numbers of robots. The success of this preliminary study demonstrates the viability of this approach for learning to coordinate, representing the first stage of implementation of a larger system for more complex coordination tasks and strategies. © 2005 IEEE.

Original languageEnglish
Title of host publication2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings
Pages2317-2324
Number of pages8
Volume3
Publication statusPublished - 2005
Event2005 IEEE Congress on Evolutionary Computation - Edinburgh, Scotland, United Kingdom
Duration: 2 Sep 20055 Sep 2005

Conference

Conference2005 IEEE Congress on Evolutionary Computation
Abbreviated titleIEEE CEC 2005
CountryUnited Kingdom
CityEdinburgh, Scotland
Period2/09/055/09/05

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  • Cite this

    Sotzing, C. C., Htay, W. M., & Congdon, C. B. (2005). GENCEM: A genetic algorithms approach to coordinated exploration and mapping with multiple autonomous robots. In 2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings (Vol. 3, pp. 2317-2324)