NDLI: An efficient stochastic clustering auction for heterogeneous robot teams

Content Provider	IEEE Xplore Digital Library
Author	Kai Zhang Collins, E.G. Barbu, A.
Copyright Year	2012
Description	Author affiliation: Department of Statistics, Florida State University, Tallahassee, 32306, USA (Barbu, A.) \|\| Center for Intelligent Systems, Control and Robotics (CISCOR), Department of Mechanical Engineering, FAMU-FSU College of Engineering, Florida A&M University-Florida State University, Tallahassee, 32310, USA (Kai Zhang; Collins, E.G.)
Abstract	Stochastic Clustering Auctions (SCAs) constitute a class of cooperative auction methods that enable improvement of the global cost of the task allocations obtained with fast greedy algorithms. Prior research had developed Contracts Sequencing Algorithms (CSAs) that are deterministic and enable transfers, swaps, and other types of contracts between team members. In contrast to CSAs, SCAs use stochastic transfers or swaps between the task clusters assigned to each team member and have algorithm parameters that can enable tradeoffs between optimality and computational and communication requirements. The first SCA was based on a “Gibbs Sampler” and constrained the stochastic cluster reallocations to simple single transfers or swaps; it is applicable to heterogeneous teams. Subsequently, a more efficient SCA was developed, based on the generalized Swendsen-Wang method; it achieves the increased efficiency by connecting tasks that appear to be synergistic and then stochastically reassigning these connected tasks, hence enabling more complex and efficient movements between clusters than the first SCA. However, its application was limited to homogeneous teams. The contribution of this work is to present an efficient SCA for heterogeneous teams; it is based on a modified Swendsen-Wang method. For centralized auctioning and homogeneous teams, extensive numerical experiments were used to provide a comparison in terms of costs and computational and communication requirements of the three SCAs and a baseline CSA. It was seen that the new SCA maintains the efficiency of the second SCA and can yield similar performance to the baseline CSA in far fewer iterations. The same metrics were used to evaluate the performance of the new SCA for heterogeneous teams. A distributed version of the new SCA was also evaluated in numerical experiments. The results show that, as expected, the distributed SCA continually improves the global performance with each iteration, but converges to a lower cost solution than the centralized SCA.
Starting Page	4806
Ending Page	4813
File Size	251592
Page Count	8
File Format	PDF
ISBN	9781467314039
ISSN	10504729
e-ISBN	9781467314053
e-ISBN	9781467314046
DOI	10.1109/ICRA.2012.6224588
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-05-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Contracts Resource management Clustering algorithms Robot kinematics Greedy algorithms Annealing
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Control and Systems Engineering Electrical and Electronic Engineering Software

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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