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

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

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

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

School of Engineering & Physical Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

8 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 language	English
Title of host publication	2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings
Pages	2317-2324
Number of pages	8
Volume	3
Publication status	Published - 2005
Event	2005 IEEE Congress on Evolutionary Computation - Edinburgh, Scotland, United Kingdom Duration: 2 Sept 2005 → 5 Sept 2005

Conference

Conference	2005 IEEE Congress on Evolutionary Computation
Abbreviated title	IEEE CEC 2005
Country/Territory	United Kingdom
City	Edinburgh, Scotland
Period	2/09/05 → 5/09/05

Cite this

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title = "GENCEM: A genetic algorithms approach to coordinated exploration and mapping with multiple autonomous robots",

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. {\textcopyright} 2005 IEEE.",

author = "Sotzing, {Chris C.} and Htay, {Win Mar} and Congdon, {Clare Bates}",

year = "2005",

language = "English",

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note = "2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005 ; Conference date: 02-09-2005 Through 05-09-2005",

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AU - Htay, Win Mar

AU - Congdon, Clare Bates

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N2 - 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.

AB - 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.

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M3 - Conference contribution

SN - 0780393635

VL - 3

SP - 2317

EP - 2324

BT - 2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings

Y2 - 2 September 2005 through 5 September 2005

ER -

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

Abstract

Conference

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