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Robust Coordination for Large Sets of Simple RoversThe ability to coordinate sets of rovers in an unknown environment is critical to the long-term success of many of NASA;s exploration missions. Such coordination policies must have the ability to adapt in unmodeled or partially modeled domains and must be robust against environmental noise and rover failures. In addition such coordination policies must accommodate a large number of rovers, without excessive and burdensome hand-tuning. In this paper we present a distributed coordination method that addresses these issues in the domain of controlling a set of simple rovers. The application of these methods allows reliable and efficient robotic exploration in dangerous, dynamic, and previously unexplored domains. Most control policies for space missions are directly programmed by engineers or created through the use of planning tools, and are appropriate for single rover missions or missions requiring the coordination of a small number of rovers. Such methods typically require significant amounts of domain knowledge, and are difficult to scale to large numbers of rovers. The method described in this article aims to address cases where a large number of rovers need to coordinate to solve a complex time dependent problem in a noisy environment. In this approach, each rover decomposes a global utility, representing the overall goal of the system, into rover-specific utilities that properly assign credit to the rover s actions. Each rover then has the responsibility to create a control policy that maximizes its own rover-specific utility. We show a method of creating rover-utilities that are "aligned" with the global utility, such that when the rovers maximize their own utility, they also maximize the global utility. In addition we show that our method creates rover-utilities that allow the rovers to create their control policies quickly and reliably. Our distributed learning method allows large sets rovers be used unmodeled domains, while providing robustness against rover failures and changing environments. In experimental simulations we show that our method scales well with large numbers of rovers in addition to being robust against noisy sensor inputs and noisy servo control. The results show that our method is able to scale to large numbers of rovers and achieves up to 400% performance improvement over standard machine learning methods.
Document ID
20060015667
Acquisition Source
Ames Research Center
Document Type
Conference Paper
Authors
Tumer, Kagan
(NASA Ames Research Center Moffett Field, CA, United States)
Agogino, Adrian
(NASA Ames Research Center Moffett Field, CA, United States)
Date Acquired
August 23, 2013
Publication Date
January 1, 2006
Publication Information
ISBN: 0-7803-7231
Subject Category
Cybernetics, Artificial Intelligence And Robotics
Report/Patent Number
IEEEAC Paper-1490
Meeting Information
Meeting: IEEE Aerospace Conference
Location: Big Sky, MT
Country: United States
Start Date: March 4, 2006
End Date: March 11, 2006
Sponsors: Institute of Electrical and Electronics Engineers
Distribution Limits
Public
Copyright
Other

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