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Coordinating Multi-Rover Systems: Evaluation Functions for Dynamic and Noisy EnvironmentsThis paper addresses the evolution of control strategies for a collective: a set of entities that collectively strives to maximize a global evaluation function that rates the performance of the full system. Directly addressing such problems by having a population of collectives and applying the evolutionary algorithm to that population is appealing, but the search space is prohibitively large in most cases. Instead, we focus on evolving control policies for each member of the collective. The fundamental issue in this approach is how to create an evaluation function for each member of the collective that is both aligned with the global evaluation function and is sensitive to the fitness changes of the member, while relatively insensitive to the fitness changes of other members. We show how to construct evaluation functions in dynamic, noisy and communication-limited collective environments. On a rover coordination problem, a control policy evolved using aligned and member-sensitive evaluations outperfoms global evaluation methods by up to 400%. More notably, in the presence of a larger number of rovers or rovers with noisy and communication limited sensors, the proposed method outperforms global evaluation by a higher percentage than in noise-free conditions with a small number of rovers.
Document ID
20050185191
Acquisition Source
Headquarters
Document Type
Preprint (Draft being sent to journal)
Authors
Turner, Kagan
(NASA Ames Research Center Moffett Field, CA, United States)
Agogino, Adrian
(NASA Ames Research Center Moffett Field, CA, United States)
Date Acquired
September 7, 2013
Publication Date
January 1, 2005
Subject Category
Numerical Analysis
Meeting Information
Meeting: Genetic and Evolutionary Computing Conference
Location: Washington, DC
Country: United States
Start Date: June 25, 2005
End Date: June 29, 2005
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.
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