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Thomas Magnanti and Stephan Kolitz. Zhao, Wei System Design and Management Program. System Design and Management Program. 2005-09-27T21:00:10Z 2005-09-27T21:00:10Z 1999 1999 http://hdl.handle.net/1721.1/29165 48198464 Thesis (S.M.)--Massachusetts Institute of Technology, System Design & Management Program, 1999. Includes bibliographical references (leaves 83-85). This thesis investigates multiple autonomous vehicle mission planning and management. It begins by introducing the basic concepts and objectives of the multivehicle mission-planning problem. Then it formulates the problem mathematically and analyzes parameters in the objective function. The solution approach uses a hierarchical mission-planning scheme to take advantage of a scalable architecture. We develop a heuristic-based algorithm to solve the multiple-vehicle mission-planning problem. The algorithm has two phases: goal-point partitioning and routing. Goal-point partitioning uses a sweep procedure to group goal-points. Routing uses an implementation of simulated annealing combined with well-known TSP heuristics. Through the computational experiments conducted on both traveling salesman problem test cases, the TSPLIB library, and randomly generated test data, the routing algorithm performs quite well. It has been able to find TSP tours within one percent of optimality, and typically within one-half of one percent. The integration of the two-phase approach provides a solution to the multiple autonomous vehicle mission planning problem. by Wei Zhao. S.M. 85 leaves, [10] leaves of plates 12331239 bytes 12330995 bytes application/pdf application/pdf 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 System Design and Management Program. Multiple autonomous vehicle mission planning and management Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="af63d1db-8007-4ed7-9971-1fb866f90d1f"> <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>Multiple autonomous vehicle mission planning and management</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>1999</PublicationDate> <Authors> <Author> <DisplayName>Zhao, Wei</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>System Design and Management Program.</Keyword> <Abstract>This thesis investigates multiple autonomous vehicle mission planning and management. It begins by introducing the basic concepts and objectives of the multivehicle mission-planning problem. Then it formulates the problem mathematically and analyzes parameters in the objective function. The solution approach uses a hierarchical mission-planning scheme to take advantage of a scalable architecture. We develop a heuristic-based algorithm to solve the multiple-vehicle mission-planning problem. The algorithm has two phases: goal-point partitioning and routing. Goal-point partitioning uses a sweep procedure to group goal-points. Routing uses an implementation of simulated annealing combined with well-known TSP heuristics. Through the computational experiments conducted on both traveling salesman problem test cases, the TSPLIB library, and randomly generated test data, the routing algorithm performs quite well. It has been able to find TSP tours within one percent of optimality, and typically within one-half of one percent. The integration of the two-phase approach provides a solution to the multiple autonomous vehicle mission planning problem.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>