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Jonathan P. How. Chen, Yu Fan, Ph. D. Massachusetts Institute of Technology Massachusetts Institute of Technology. Department of Aeronautics and Astronautics. Massachusetts Institute of Technology. Department of Aeronautics and Astronautics 2015-02-05T18:21:49Z 2015-02-05T18:21:49Z 2014 2014 http://hdl.handle.net/1721.1/93795 900608794 Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2014. Cataloged from PDF version of thesis. Includes bibliographical references (pages 99-104). Multi-agent robotic systems have attracted the interests of both researchers and practitioners because they provide more capabilities and afford greater flexibility than single-agent systems. Coordination of individual agents within large teams is often challenging because of the combinatorial nature of such problems. In particular, the number of possible joint configurations is the product of that of every agent. Further, real world applications often contain various sources of uncertainties. This thesis investigates techniques to address the scalability issue of multi-agent planning under uncertainties. This thesis develops a novel hierarchical decomposition approach (HD-MMDP) for solving Multi-agent Markov Decision Processes (MMDPs), which is a natural framework for formulating stochastic sequential decision-making problems. In particular, the HD-MMDP algorithm builds a decomposition structure by exploiting coupling relationships in the reward function. A number of smaller subproblems are formed and are solved individually. The planning spaces of each subproblem are much smaller than that of the original problem, which improves the computational efficiency, and the solutions to the subproblems can be combined to form a solution (policy) to the original problem. The HD-MMDP algorithm is applied on a ten agent persistent search and track (PST) mission and shows more than 35% improvement over an existing algorithm developed specifically for this domain. This thesis also contributes to the development of the software infrastructure that enables hardware experiments involving multiple robots. In particular, the thesis presents a novel optimization based multi-agent path planning algorithm, which was tested in simulation and hardware (quadrotor) experiment. The HD-MMDP algorithm is also used to solve a multi-agent intruder monitoring mission implemented using real robots. by Yu Fan Chen. S.M. 104 pages eng Massachusetts Institute of Technology M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 Aeronautics and Astronautics. Hierarchical decomposition of multi-agent Markov decision processes with application to health aware planning Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="2f1ea20a-010a-4fc4-beee-18f328545bcf"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Hierarchical decomposition of multi-agent Markov decision processes with application to health aware planning</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2014</PublicationDate> <Authors> <Author> <DisplayName>Chen, Yu Fan, Ph. D. Massachusetts Institute of Technology</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Aeronautics and Astronautics.</Keyword> <Abstract>Multi-agent robotic systems have attracted the interests of both researchers and practitioners because they provide more capabilities and afford greater flexibility than single-agent systems. Coordination of individual agents within large teams is often challenging because of the combinatorial nature of such problems. In particular, the number of possible joint configurations is the product of that of every agent. Further, real world applications often contain various sources of uncertainties. This thesis investigates techniques to address the scalability issue of multi-agent planning under uncertainties. This thesis develops a novel hierarchical decomposition approach (HD-MMDP) for solving Multi-agent Markov Decision Processes (MMDPs), which is a natural framework for formulating stochastic sequential decision-making problems. In particular, the HD-MMDP algorithm builds a decomposition structure by exploiting coupling relationships in the reward function. A number of smaller subproblems are formed and are solved individually. The planning spaces of each subproblem are much smaller than that of the original problem, which improves the computational efficiency, and the solutions to the subproblems can be combined to form a solution (policy) to the original problem. The HD-MMDP algorithm is applied on a ten agent persistent search and track (PST) mission and shows more than 35% improvement over an existing algorithm developed specifically for this domain. This thesis also contributes to the development of the software infrastructure that enables hardware experiments involving multiple robots. In particular, the thesis presents a novel optimization based multi-agent path planning algorithm, which was tested in simulation and hardware (quadrotor) experiment. The HD-MMDP algorithm is also used to solve a multi-agent intruder monitoring mission implemented using real robots.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>